1217 lines
334 KiB
Text
1217 lines
334 KiB
Text
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "a6ba3eb0-8f27-4ebd-b407-3f25f449c6bf",
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"metadata": {},
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"source": [
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"# Imports"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 86,
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"id": "bd9299aa-a531-468c-b04b-798b06315f41",
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"metadata": {},
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"outputs": [],
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"source": [
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"# pyright: reportUnknownArgumentType=false, reportCallIssue=false, reportAttributeAccessIssue=false, reportOptionalSubscript=false, reportArgumentType=false\n",
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"from rich.console import Console\n",
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"from rich.table import Table\n",
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"from rich.theme import Theme\n",
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"from rich.logging import RichHandler\n",
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"\n",
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"from finesse.model import Model\n",
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"from finesse.solutions import SeriesSolution\n",
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"from finesse.detectors import PowerDetector\n",
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"from finesse.analysis.actions import (\n",
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" TemporaryParameters,\n",
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" Change,\n",
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" Maximize,\n",
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" Minimize,\n",
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" Series,\n",
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" FrequencyResponse4,\n",
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" FrequencyResponse,\n",
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" Noxaxis,\n",
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")\n",
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"from finesse.components import Mirror, ReadoutDC\n",
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"from finesse.exceptions import ModelMissingAttributeError\n",
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"\n",
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"from pathlib import Path\n",
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"\n",
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"from matplotlib.pyplot import figure, show\n",
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"\n",
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"\n",
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"from numpy import (\n",
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" geomspace,\n",
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" pi,\n",
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" angle,\n",
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" diff,\n",
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" loadtxt,\n",
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" load,\n",
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" sqrt,\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "4c038d40-1d01-49cb-9182-a9a0e94d0d40",
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"metadata": {},
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"outputs": [],
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"source": [
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"from gettext import install\n",
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"from logging import getLogger, basicConfig, INFO, WARNING"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "12fc3934-0929-479f-9431-95bd8788d7ee",
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"metadata": {},
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"outputs": [],
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"source": [
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"from utils import (\n",
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" DisplayData,\n",
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" display_displaydata,\n",
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" fix_dark_fringe,\n",
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" get_QNLS,\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "5d4f2612-c5ea-4b21-a326-7074022966bc",
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"metadata": {},
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"outputs": [],
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"source": [
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"install(__name__)\n",
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"theme = Theme(\n",
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" {\n",
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" \"strong\": \"cyan underline\",\n",
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" \"result\": \"red bold\",\n",
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" }\n",
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")\n",
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"console = Console(theme=theme)\n",
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"basicConfig(\n",
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" level=WARNING,\n",
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" format=\"%(message)s\",\n",
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" datefmt=\"[%X]\",\n",
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" handlers=[RichHandler(console=console, rich_tracebacks=True)],\n",
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")\n",
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"logger = getLogger(__name__)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "eb7d2340-c817-4309-9599-6d58070ff4ab",
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"metadata": {},
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"source": [
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"## Paramètres généraux"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "8fc23eea-145e-4641-93e9-6f8989edca96",
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"metadata": {},
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"outputs": [],
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"source": [
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"C_POWER = 25 # en Whatt\n",
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"C_DARK_FRINGE = 8e-3 # en Whatt\n",
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"C_PRECISION = 100 # number of points in simulation\n",
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"C_DEBUG = False # if some figure should be displayed"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3052aa2b-350e-4eb3-b31c-a4204ab84dac",
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"metadata": {},
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"source": [
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"## Modèle simplifié de Virgo"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 65,
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"id": "d32480d0-8525-478a-9af5-b1a1c8b30f1d",
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"metadata": {},
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"outputs": [],
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"source": [
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"model: Model = Model()\n",
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"model.phase_config(zero_k00=False, zero_tem00_gouy=True)\n",
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"\n",
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"model.parse(Path(\"model.kat\").read_text())\n",
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"model.lambda0 = model.get(\"wavelength\")\n",
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"# model.SR.xbeta = 2e-6 # yaw rotation of SR\n",
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"model.laser.P = C_POWER\n",
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"\n",
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"if C_DEBUG:\n",
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" # Show model elements\n",
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" graph = model.plot_graph()\n",
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" show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 66,
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"id": "370c1750-922a-4235-954f-dd8116a7a8bd",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<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\">[13:10:28] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO </span> Adding B1 to the model <a href=\"file:///tmp/ipykernel_17169/2913538361.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2913538361.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///tmp/ipykernel_17169/2913538361.py#12\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">12</span></a>\n",
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"</pre>\n"
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],
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"text/plain": [
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"\u001b[2;36m[13:10:28]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m Adding B1 to the model \u001b]8;id=426724;file:///tmp/ipykernel_17169/2913538361.py\u001b\\\u001b[2m2913538361.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=730231;file:///tmp/ipykernel_17169/2913538361.py#12\u001b\\\u001b[2m12\u001b[0m\u001b]8;;\u001b\\\n"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"try:\n",
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" model.get(\"B1\")\n",
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" logger.info(\"B1 already exists\")\n",
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"except ModelMissingAttributeError:\n",
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" model.add(\n",
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" ReadoutDC(\n",
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" \"B1\",\n",
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" output_detectors=True,\n",
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" optical_node=model.SDB1.p2.o,\n",
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" )\n",
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" )\n",
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" logger.info(\"Adding B1 to the model\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 67,
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"id": "b742cd14-2149-437b-9194-249cf40849b1",
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"metadata": {},
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"outputs": [],
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"source": [
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"result = model.run(\n",
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" TemporaryParameters(\n",
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" Series(\n",
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" Change(\n",
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" {\n",
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" \"SR.misaligned\": True,\n",
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" \"PR.misaligned\": True,\n",
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" }\n",
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" ),\n",
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" Maximize(\n",
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" model.get(\"NE_p1\"),\n",
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" model.get(\"NORTH_ARM.DC\"),\n",
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" bounds=[-180, 180],\n",
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" tol=1e-14,\n",
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" ),\n",
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" Maximize(\n",
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" model.get(\"WE_p1\"),\n",
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" model.get(\"WEST_ARM.DC\"),\n",
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" bounds=[-180, 180],\n",
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" tol=1e-14,\n",
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" ),\n",
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" Minimize(\n",
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" model.get(\"SR_p2\"),\n",
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" model.get(\"MICH.DC\"),\n",
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" bounds=[-180, 180],\n",
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" tol=1e-14,\n",
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" ),\n",
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" Change(\n",
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" {\n",
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" \"PR.misaligned\": False,\n",
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" }\n",
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" ),\n",
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" Maximize(\n",
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" model.get(\"PR_p2\"),\n",
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" model.get(\"PRCL.DC\"),\n",
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" bounds=[-180, 180],\n",
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" tol=1e-14,\n",
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" ),\n",
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" Change(\n",
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" {\n",
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" \"SR.misaligned\": False,\n",
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" }\n",
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" ),\n",
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" Maximize(\n",
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" model.get(\"B1_DC\"),\n",
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" model.get(\"SRCL.DC\"),\n",
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" bounds=[-180, 180],\n",
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" tol=1e-14,\n",
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" ),\n",
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" Change(\n",
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" {\n",
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" \"SRCL.DC\": -90,\n",
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" },\n",
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" relative=True,\n",
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" ),\n",
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" ),\n",
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" exclude=[\n",
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" \"NE.phi\",\n",
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" \"NI.phi\",\n",
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" \"WE.phi\",\n",
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" \"WI.phi\",\n",
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" \"SR.phi\",\n",
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" \"PR.phi\",\n",
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" \"NORTH_ARM.DC\",\n",
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" \"WEST_ARM.DC\",\n",
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" \"DARM.DC\",\n",
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" \"MICH.DC\",\n",
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" \"PRCL.DC\",\n",
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" \"SRCL.DC\",\n",
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" \"SR.misaligned\",\n",
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" ],\n",
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" ),\n",
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")\n",
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"# model.B1.select_mask(exclude=(0, 0))\n",
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"model.modes(maxtem=3)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 68,
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"id": "14c711bb-820e-4ee1-91e9-b1d29350513d",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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",
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"text/plain": [
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"<Figure size 1300x1000 with 6 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/html": [
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"<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\">0.001617431640625</span> with <span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">15</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\">0.007933</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",
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"</pre>\n"
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],
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"text/plain": [
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"\u001b[1;31m0.001617431640625\u001b[0m with \u001b[1;31m15\u001b[0m\u001b[1;31m steps\u001b[0m give \u001b[1;31m0.007933\u001b[0m\u001b[1;31m mW\u001b[0m on the \u001b[4;36mdark fringe\u001b[0m\n"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"logger.setLevel(WARNING)\n",
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"for handler in logger.handlers:\n",
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" handler.setLevel(WARNING)\n",
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"display_displaydata(\n",
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" model,\n",
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" [\n",
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" DisplayData(\"NORTH_ARM\", 10),\n",
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" DisplayData(\"WEST_ARM\", 10),\n",
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" DisplayData(\"PRCL\", 10),\n",
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" DisplayData(\"MICH\", 10),\n",
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" DisplayData(\"DARM\", 10),\n",
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" DisplayData(\"CARM\", 10),\n",
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" ],\n",
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")\n",
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"number, power = fix_dark_fringe(model, C_DARK_FRINGE)\n",
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"console.print(\n",
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" \"[result]{dof}[/result] with [result]{number} steps[/result] give [result]{power:.6f} mW[/result] on the [strong]dark fringe[/strong]\".format(\n",
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" number=number,\n",
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" dof=model.DARM.DC,\n",
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" power=power,\n",
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" )\n",
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")\n",
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"logger.setLevel(INFO)\n",
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"for handler in logger.handlers:\n",
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" handler.setLevel(INFO)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 69,
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"id": "66c9be03-cedc-4d07-acbd-3269f3143cd4",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<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",
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"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓\n",
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"┃<span style=\"font-weight: bold\"> position </span>┃<span style=\"font-weight: bold\"> puissance (W) </span>┃\n",
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"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩\n",
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"│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> Injection </span>│<span style=\"color: #008080; text-decoration-color: #008080\"> 25.0 </span>│\n",
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"│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> PR </span>│<span style=\"color: #008080; text-decoration-color: #008080\"> 3.5299027335746955 </span>│\n",
|
|
"│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> cavité de recyclage de puissance </span>│<span style=\"color: #008080; text-decoration-color: #008080\"> 965.4281013768152 </span>│\n",
|
|
"│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> cavité ouest </span>│<span style=\"color: #008080; text-decoration-color: #008080\"> 135283.21834637853 </span>│\n",
|
|
"│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> cavité nord </span>│<span style=\"color: #008080; text-decoration-color: #008080\"> 133182.10264713175 </span>│\n",
|
|
"│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> frange noire </span>│<span style=\"color: #008080; text-decoration-color: #008080\"> 0.007934010101951626 </span>│\n",
|
|
"│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> SNEB </span>│<span style=\"color: #008080; text-decoration-color: #008080\"> 0.5977795833152771 </span>│\n",
|
|
"│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> SWEB </span>│<span style=\"color: #008080; text-decoration-color: #008080\"> 0.5816474683593496 </span>│\n",
|
|
"│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> SDB1 </span>│<span style=\"color: #008080; text-decoration-color: #008080\"> 0.007932891406527252 </span>│\n",
|
|
"└──────────────────────────────────┴──────────────────────┘\n",
|
|
"</pre>\n"
|
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],
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"text/plain": [
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"\u001b[3m Puissances dans l'interferomètre \u001b[0m\n",
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"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓\n",
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"┃\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",
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"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩\n",
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"│\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",
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"│\u001b[37m \u001b[0m\u001b[37mPR \u001b[0m\u001b[37m \u001b[0m│\u001b[36m \u001b[0m\u001b[36m3.5299027335746955 \u001b[0m\u001b[36m \u001b[0m│\n",
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"│\u001b[37m \u001b[0m\u001b[37mcavité de recyclage de puissance\u001b[0m\u001b[37m \u001b[0m│\u001b[36m \u001b[0m\u001b[36m965.4281013768152 \u001b[0m\u001b[36m \u001b[0m│\n",
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"│\u001b[37m \u001b[0m\u001b[37mcavité ouest \u001b[0m\u001b[37m \u001b[0m│\u001b[36m \u001b[0m\u001b[36m135283.21834637853 \u001b[0m\u001b[36m \u001b[0m│\n",
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"│\u001b[37m \u001b[0m\u001b[37mcavité nord \u001b[0m\u001b[37m \u001b[0m│\u001b[36m \u001b[0m\u001b[36m133182.10264713175 \u001b[0m\u001b[36m \u001b[0m│\n",
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"│\u001b[37m \u001b[0m\u001b[37mfrange noire \u001b[0m\u001b[37m \u001b[0m│\u001b[36m \u001b[0m\u001b[36m0.007934010101951626\u001b[0m\u001b[36m \u001b[0m│\n",
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"│\u001b[37m \u001b[0m\u001b[37mSNEB \u001b[0m\u001b[37m \u001b[0m│\u001b[36m \u001b[0m\u001b[36m0.5977795833152771 \u001b[0m\u001b[36m \u001b[0m│\n",
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"│\u001b[37m \u001b[0m\u001b[37mSWEB \u001b[0m\u001b[37m \u001b[0m│\u001b[36m \u001b[0m\u001b[36m0.5816474683593496 \u001b[0m\u001b[36m \u001b[0m│\n",
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"│\u001b[37m \u001b[0m\u001b[37mSDB1 \u001b[0m\u001b[37m \u001b[0m│\u001b[36m \u001b[0m\u001b[36m0.007932891406527252\u001b[0m\u001b[36m \u001b[0m│\n",
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"└──────────────────────────────────┴──────────────────────┘\n"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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"<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",
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"┏━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
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"┃<span style=\"font-weight: bold\"> nom </span>┃<span style=\"font-weight: bold\"> valeur </span>┃\n",
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"┡━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
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"│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> Bras nord </span>│<span style=\"color: #800080; text-decoration-color: #800080\"> 6.592588499188424e-06 </span>│\n",
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"│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> Bras ouest </span>│<span style=\"color: #800080; text-decoration-color: #800080\"> -5.8479547500610346e-05 </span>│\n",
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"│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> PR </span>│<span style=\"color: #800080; text-decoration-color: #800080\"> -44.946681098699614 </span>│\n",
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"│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> SR </span>│<span style=\"color: #800080; text-decoration-color: #800080\"> -135.09400848257525 </span>│\n",
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"│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> MICH </span>│<span style=\"color: #800080; text-decoration-color: #800080\"> -89.79029219055182 </span>│\n",
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"└────────────┴─────────────────────────┘\n",
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"</pre>\n"
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"\u001b[3m DOF dans l'interferomètre \u001b[0m\n",
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"┏━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
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"┃\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",
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"┡━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
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"│\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",
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"│\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",
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"│\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",
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"│\u001b[37m \u001b[0m\u001b[37mSR \u001b[0m\u001b[37m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m-135.09400848257525 \u001b[0m\u001b[35m \u001b[0m│\n",
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"│\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",
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"└────────────┴─────────────────────────┘\n"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/html": [
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
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"┃<span style=\"font-weight: bold\"> nom </span>┃<span style=\"font-weight: bold\"> valeur </span>┃\n",
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"┡━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
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"│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> FSR </span>│<span style=\"color: #008080; text-decoration-color: #008080\"> 49968.74091606107 </span>│\n",
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"│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> Loss </span>│<span style=\"color: #008080; text-decoration-color: #008080\"> 0.013848722846499961 </span>│\n",
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"│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> Finesse </span>│<span style=\"color: #008080; text-decoration-color: #008080\"> 450.5516190359727 </span>│\n",
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"│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> FWHM </span>│<span style=\"color: #008080; text-decoration-color: #008080\"> 110.90569605093683 </span>│\n",
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"│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> Storage time </span>│<span style=\"color: #008080; text-decoration-color: #008080\"> 0.0028700950223295757 </span>│\n",
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"│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> Pole </span>│<span style=\"color: #008080; text-decoration-color: #008080\"> 55.45284802546841 </span>│\n",
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"│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> Round trip length </span>│<span style=\"color: #008080; text-decoration-color: #008080\"> 5999.6 </span>│\n",
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"│<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",
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"│<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",
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"│<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",
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"│<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",
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"│<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",
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"│<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",
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"│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> Resolution </span>│<span style=\"color: #008080; text-decoration-color: #008080\"> [52.80206387 52.80206387] </span>│\n",
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"│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> Stable </span>│<span style=\"color: #008080; text-decoration-color: #008080\"> True </span>│\n",
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"│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> Critically stable </span>│<span style=\"color: #008080; text-decoration-color: #008080\"> False </span>│\n",
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"└───────────────────────┴─────────────────────────────────┘\n",
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"</pre>\n"
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],
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"text/plain": [
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"┏━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
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"┃\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",
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"┡━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
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"│\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",
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"│\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",
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"│\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",
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"│\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",
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"│\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",
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"│\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",
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"│\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",
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"│\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",
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"│\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",
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"│\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",
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"│\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",
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"│\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",
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"│\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",
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"│\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",
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"│\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",
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"│\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",
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"└───────────────────────┴─────────────────────────────────┘\n"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
|
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"solution = model.run(Noxaxis())\n",
|
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"console = Console()\n",
|
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"table = Table(title=\"Puissances dans l'interferomètre\")\n",
|
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"table.add_column(\"position\", justify=\"left\", style=\"white\")\n",
|
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"table.add_column(\"puissance (W)\", justify=\"left\", style=\"cyan\")\n",
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"\n",
|
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"table.add_row(\"Injection\", str(model.get(\"laser\").P.eval()))\n",
|
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"table.add_row(\"PR\", str(solution[\"PR_p1\"]))\n",
|
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"table.add_row(\n",
|
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" \"cavité de recyclage de puissance\", str(solution[\"PR_p2\"])\n",
|
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")\n",
|
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"table.add_row(\"cavité ouest\", str(solution[\"WE_p1\"]))\n",
|
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"table.add_row(\"cavité nord\", str(solution[\"NE_p1\"]))\n",
|
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"table.add_row(\"frange noire\", str(solution[\"SR_p2\"]))\n",
|
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"table.add_row(\"SNEB\", str(solution[\"SNEB_DC\"]))\n",
|
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"table.add_row(\"SWEB\", str(solution[\"SWEB_DC\"]))\n",
|
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"table.add_row(\"SDB1\", str(solution[\"SDB1_DC\"]))\n",
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"\n",
|
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"console.print(table)\n",
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"\n",
|
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"table = Table(title=\"DOF dans l'interferomètre\")\n",
|
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"table.add_column(\"nom\", justify=\"left\", style=\"white\")\n",
|
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"table.add_column(\"valeur\", justify=\"left\", style=\"magenta\")\n",
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"\n",
|
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"table.add_row(\"Bras nord\", str(model.get(\"NORTH_ARM.DC\")))\n",
|
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"table.add_row(\"Bras ouest\", str(model.get(\"WEST_ARM.DC\")))\n",
|
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"table.add_row(\"PR\", str(model.get(\"PRCL.DC\")))\n",
|
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"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": 82,
|
|
"id": "b040e22b-4732-4835-8c84-cf85dc045baf",
|
|
"metadata": {},
|
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"outputs": [
|
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{
|
|
"data": {
|
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"text/html": [
|
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">ColorSequenceRegistry; available colormaps:\n",
|
|
"<span style=\"color: #008000; text-decoration-color: #008000\">'tab10'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'tab20'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'tab20b'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'tab20c'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'Pastel1'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'Pastel2'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'Paired'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'Accent'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'Dark2'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'Set1'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'Set2'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'Set3'</span>, \n",
|
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"<span style=\"color: #008000; text-decoration-color: #008000\">'petroff10'</span>\n",
|
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"</pre>\n"
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],
|
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"text/plain": [
|
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"ColorSequenceRegistry; available colormaps:\n",
|
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"\u001b[32m'tab10'\u001b[0m, \u001b[32m'tab20'\u001b[0m, \u001b[32m'tab20b'\u001b[0m, \u001b[32m'tab20c'\u001b[0m, \u001b[32m'Pastel1'\u001b[0m, \u001b[32m'Pastel2'\u001b[0m, \u001b[32m'Paired'\u001b[0m, \u001b[32m'Accent'\u001b[0m, \u001b[32m'Dark2'\u001b[0m, \u001b[32m'Set1'\u001b[0m, \u001b[32m'Set2'\u001b[0m, \u001b[32m'Set3'\u001b[0m, \n",
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"\u001b[32m'petroff10'\u001b[0m\n"
|
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]
|
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},
|
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"metadata": {},
|
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"output_type": "display_data"
|
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}
|
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],
|
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"source": [
|
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"from matplotlib import color_sequences\n",
|
|
"console.print(color_sequences)"
|
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]
|
|
},
|
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{
|
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"cell_type": "code",
|
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"execution_count": 85,
|
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"id": "bacb32e0-aa84-4c35-9e96-75edc832be17",
|
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"metadata": {},
|
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"outputs": [
|
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{
|
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"ename": "Exception",
|
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"evalue": "fsig value must be > 0 Hz",
|
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"output_type": "error",
|
|
"traceback": [
|
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"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
|
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"\u001b[31mException\u001b[39m Traceback (most recent call last)",
|
|
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[85]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m 1\u001b[39m B1_detector = model.SDB1.p2.o\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfsig\u001b[49m\u001b[43m.\u001b[49m\u001b[43mf\u001b[49m = \u001b[32m0\u001b[39m\n\u001b[32m 5\u001b[39m solution = model.run(\n\u001b[32m 6\u001b[39m FrequencyResponse4(\n\u001b[32m 7\u001b[39m geomspace(\u001b[32m5\u001b[39m, \u001b[32m10000\u001b[39m, C_PRECISION),\n\u001b[32m (...)\u001b[39m\u001b[32m 13\u001b[39m )\n\u001b[32m 14\u001b[39m )\n\u001b[32m 16\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m solution \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
|
|
"\u001b[36mFile \u001b[39m\u001b[32m~/codes/python/finesse/finesse-simulation-04/.venv/lib/python3.13/site-packages/finesse/freeze.py:54\u001b[39m, in \u001b[36mcanFreeze.<locals>.frozensetattr\u001b[39m\u001b[34m(self, name, value)\u001b[39m\n\u001b[32m 50\u001b[39m n = \u001b[38;5;28mself\u001b[39m.\u001b[34m__class__\u001b[39m.\u001b[34m__name__\u001b[39m\n\u001b[32m 52\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m'\u001b[39m\u001b[33m does not have attribute called \u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m'\u001b[39m\u001b[33m\"\u001b[39m % (n, name))\n\u001b[32m---> \u001b[39m\u001b[32m54\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[34;43m__setattr__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n",
|
|
"\u001b[36mFile \u001b[39m\u001b[32msrc/finesse/parameter.pyx:873\u001b[39m, in \u001b[36mfinesse.parameter.parameterproperty.__set__\u001b[39m\u001b[34m()\u001b[39m\n",
|
|
"\u001b[36mFile \u001b[39m\u001b[32msrc/finesse/parameter.pyx:852\u001b[39m, in \u001b[36mfinesse.parameter.parameterproperty.__fset\u001b[39m\u001b[34m()\u001b[39m\n",
|
|
"\u001b[36mFile \u001b[39m\u001b[32msrc/finesse/parameter.pyx:598\u001b[39m, in \u001b[36mfinesse.parameter.Parameter._set\u001b[39m\u001b[34m()\u001b[39m\n",
|
|
"\u001b[36mFile \u001b[39m\u001b[32msrc/finesse/parameter.pyx:581\u001b[39m, in \u001b[36mfinesse.parameter.Parameter.value.__set__\u001b[39m\u001b[34m()\u001b[39m\n",
|
|
"\u001b[36mFile \u001b[39m\u001b[32msrc/finesse/parameter.pyx:557\u001b[39m, in \u001b[36mfinesse.parameter.Parameter._set_value\u001b[39m\u001b[34m()\u001b[39m\n",
|
|
"\u001b[36mFile \u001b[39m\u001b[32msrc/finesse/parameter.pyx:978\u001b[39m, in \u001b[36mfinesse.parameter.Validator.__call__\u001b[39m\u001b[34m()\u001b[39m\n",
|
|
"\u001b[36mFile \u001b[39m\u001b[32msrc/finesse/frequency.pyx:231\u001b[39m, in \u001b[36mfinesse.frequency.Fsig._validate_fsig\u001b[39m\u001b[34m()\u001b[39m\n",
|
|
"\u001b[31mException\u001b[39m: fsig value must be > 0 Hz"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"B1_detector = model.SDB1.p2.o\n",
|
|
"\n",
|
|
"solution = model.run(\n",
|
|
" FrequencyResponse4(\n",
|
|
" geomspace(5, 10000, C_PRECISION),\n",
|
|
" [\"DARM\"],\n",
|
|
" [\n",
|
|
" (B1_detector, +model.fsig.f.ref),\n",
|
|
" (B1_detector, -model.fsig.f.ref),\n",
|
|
" ],\n",
|
|
" )\n",
|
|
")\n",
|
|
"\n",
|
|
"if solution is None:\n",
|
|
" raise Exception(\"Failed to compute TF\")\n",
|
|
"\n",
|
|
"assert solution.out.shape == (\n",
|
|
" C_PRECISION,\n",
|
|
" len(solution.outputs),\n",
|
|
" 1,\n",
|
|
" len(model.modes()),\n",
|
|
")\n",
|
|
"\n",
|
|
"\"\"\"\n",
|
|
"console.print(solution.outputs)\n",
|
|
"console.print(solution.f)\n",
|
|
"console.print(solution.out[:, 1, 0, 3])\n",
|
|
"console.print(solution[(B1_detector, + model.fsig.f.ref),\"DARM\"])\n",
|
|
"\"\"\"\n",
|
|
"\n",
|
|
"colors = [\n",
|
|
" 'blue',\n",
|
|
" 'orange',\n",
|
|
" 'green',\n",
|
|
" 'red',\n",
|
|
" 'purple',\n",
|
|
" 'brown',\n",
|
|
" 'pink',\n",
|
|
" 'cyan',\n",
|
|
" 'olive',\n",
|
|
" ''\n",
|
|
"]\n",
|
|
"Figure = figure()\n",
|
|
"for i in range(len(model.modes())):\n",
|
|
" mode = model.modes()[i]\n",
|
|
" _ = Figure.gca().loglog(\n",
|
|
" solution.f,\n",
|
|
" abs(solution.out[:, 0, 0, i]),\n",
|
|
" label=\"{}\".format(mode),\n",
|
|
" )\n",
|
|
" _ = Figure.gca().loglog(\n",
|
|
" solution.f,\n",
|
|
" abs(solution.out[:, 1, 0, i]),\n",
|
|
" label=\"{}\".format(mode),\n",
|
|
" linestyle=\"--\",\n",
|
|
" )\n",
|
|
"_ = Figure.gca().legend()\n",
|
|
"show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"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": [
|
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"/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",
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|
" self.__sim.__enter__()\n"
|
|
]
|
|
},
|
|
{
|
|
"ename": "KeyError",
|
|
"evalue": "\"This solution does not have an output called '<OpticalNode SDB1.p2.o @ 0x7e5e18455700> <class 'finesse.components.node.OpticalNode'>'. Allowable OUTPUTS=[(<OpticalNode SDB1.p2.o @ 0x7e5e18455700>, <Symbolic='+fsig.f' @ 0x7e5e14f252b0>), (<OpticalNode SDB1.p2.o @ 0x7e5e18455700>, <Symbolic='-fsig.f' @ 0x7e5e14f25390>)]\"",
|
|
"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[14]\u001b[39m\u001b[32m, line 34\u001b[39m\n\u001b[32m 26\u001b[39m show()\n\u001b[32m 28\u001b[39m solution = model.run(\n\u001b[32m 29\u001b[39m FrequencyResponse4(\n\u001b[32m 30\u001b[39m geomspace(\u001b[32m5\u001b[39m, \u001b[32m10000\u001b[39m, C_PRECISION), [\u001b[33m\"\u001b[39m\u001b[33mDARM\u001b[39m\u001b[33m\"\u001b[39m], [(B1_detector, + model.fsig.f.ref), (B1_detector, - model.fsig.f.ref)]\n\u001b[32m 31\u001b[39m )\n\u001b[32m 32\u001b[39m )\n\u001b[32m 33\u001b[39m maximum_amplitude_step: \u001b[38;5;28mfloat\u001b[39m = \u001b[38;5;28mmax\u001b[39m(\n\u001b[32m---> \u001b[39m\u001b[32m34\u001b[39m \u001b[38;5;28mabs\u001b[39m(diff(angle(\u001b[43msolution\u001b[49m\u001b[43m[\u001b[49m\u001b[43mB1_detector\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)))\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[32m 45\u001b[39m console.print(\n\u001b[32m 46\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mLe [strong]pôle[/strong] de la fonction de transfert [strong]DARM[/strong] est à [result]\u001b[39m\u001b[38;5;132;01m{:.1f}\u001b[39;00m\u001b[33m[/result] Hz\u001b[39m\u001b[33m\"\u001b[39m.format(\n\u001b[32m 47\u001b[39m solution.f[pole_index]\n\u001b[32m 48\u001b[39m )\n\u001b[32m 49\u001b[39m )\n",
|
|
"\u001b[36mFile \u001b[39m\u001b[32m~/codes/python/finesse/finesse-simulation-04/.venv/lib/python3.13/site-packages/finesse/analysis/actions/lti.py:196\u001b[39m, in \u001b[36mFrequencyResponseSolution.__getitem__\u001b[39m\u001b[34m(self, key, reversed)\u001b[39m\n\u001b[32m 184\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\n\u001b[32m 185\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Provide 2 keys [output, input] to select a transfer function\u001b[39;00m\n\u001b[32m 186\u001b[39m \u001b[33;03m for indexing this FrequencyResponseSolution, if you want to\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 193\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m 194\u001b[39m )\n\u001b[32m 195\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m196\u001b[39m o_idx, i_idx = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moutputs_inputs_indices\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m[\u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[43m[\u001b[49m\u001b[32;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 197\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.out[\u001b[38;5;28mslice\u001b[39m(\u001b[38;5;28;01mNone\u001b[39;00m), o_idx, i_idx]\n",
|
|
"\u001b[36mFile \u001b[39m\u001b[32m~/codes/python/finesse/finesse-simulation-04/.venv/lib/python3.13/site-packages/finesse/analysis/actions/lti.py:168\u001b[39m, in \u001b[36mFrequencyResponseSolution.outputs_inputs_indices\u001b[39m\u001b[34m(self, outputs, inputs, reversed)\u001b[39m\n\u001b[32m 166\u001b[39m result = \u001b[38;5;28mself\u001b[39m.outputs_inputs_indices(inputs, outputs, \u001b[38;5;28mreversed\u001b[39m=\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[32m 167\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m168\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m ex \u001b[38;5;66;03m# If the reversed failed then re-raise original exception\u001b[39;00m\n\u001b[32m 169\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 170\u001b[39m deprecation_warning(\n\u001b[32m 171\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mFrequencyResponseSolution has changed to use [output, input], you seemed to have used [input, output] so returning that.\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m 172\u001b[39m \u001b[33m\"\u001b[39m\u001b[33m3.0\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m 173\u001b[39m )\n",
|
|
"\u001b[36mFile \u001b[39m\u001b[32m~/codes/python/finesse/finesse-simulation-04/.venv/lib/python3.13/site-packages/finesse/analysis/actions/lti.py:132\u001b[39m, in \u001b[36mFrequencyResponseSolution.outputs_inputs_indices\u001b[39m\u001b[34m(self, outputs, inputs, reversed)\u001b[39m\n\u001b[32m 130\u001b[39m out_idx.append(out_key)\n\u001b[32m 131\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m out_key \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.outputs:\n\u001b[32m--> \u001b[39m\u001b[32m132\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\n\u001b[32m 133\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mThis solution does not have an output called \u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mout_key\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(out_key)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m'\u001b[39m\u001b[33m. Allowable OUTPUTS=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m.outputs\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m 134\u001b[39m )\n\u001b[32m 135\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 136\u001b[39m out_idx.append(\u001b[38;5;28mself\u001b[39m.outputs.index(out_key))\n",
|
|
"\u001b[31mKeyError\u001b[39m: \"This solution does not have an output called '<OpticalNode SDB1.p2.o @ 0x7e5e18455700> <class 'finesse.components.node.OpticalNode'>'. Allowable OUTPUTS=[(<OpticalNode SDB1.p2.o @ 0x7e5e18455700>, <Symbolic='+fsig.f' @ 0x7e5e14f252b0>), (<OpticalNode SDB1.p2.o @ 0x7e5e18455700>, <Symbolic='-fsig.f' @ 0x7e5e14f25390>)]\""
|
|
]
|
|
}
|
|
],
|
|
"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": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"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": "3.13.2"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|