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Laura Toribio San Cipriano
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Adapt fa_following_errors_fft_sitcom2117.py code for TimeSquare
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‎sst/mtm1m3/M1M3_Force_actuator_following_errors_FFT.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "36d5a662-7a3e-4241-b20d-4d04bf99235a",
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"metadata": {},
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"source": [
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"# M1M3 Force Actuator following errors FFT"
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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": null,
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"id": "afee1464-e220-4f16-a4c8-cb4f14af9723",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Times Square parameters\n",
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"t_start = \"2025-05-27T18:30:15\"\n",
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"t_end = \"2025-05-27T18:30:24\""
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]
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},
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{
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"cell_type": "markdown",
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"id": "0955ef8a-f61a-4ea2-8e79-4d3c3ec8b77c",
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"metadata": {},
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"source": [
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"> **Note:** Please select a short time range when running this code. \n",
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"> For example:\n",
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"> ```python\n",
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"> t_start = \"2025-05-27T18:30:15\"\n",
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"> t_end = \"2025-05-27T18:30:24\"\n",
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"> ```\n",
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"> In this example, the time range is only 9 seconds, but the code may still take a while to run. \n",
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"> Selecting longer time intervals (e.g., several minutes or hours) can significantly increase execution time.\n",
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">\n",
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"> In the future, the code will be optimized to efficiently handle full observation days.\n",
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">\n",
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"> Additionally, future versions will reduce the number of plots shown by filtering out force actuators with no\n",
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"> significant behavior, helping focus the analysis on potentially problematic components.\n"
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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": null,
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"id": "8eb32444-5f61-48ae-a663-3d49c35d1d45",
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"import os\n",
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"import argparse\n",
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"from astropy.time import Time, TimeDelta"
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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": null,
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"id": "e1e1d9b1-997f-4c35-91f3-b295026633c9",
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"metadata": {},
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"outputs": [],
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"source": [
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"from lsst.summit.utils.tmaUtils import TMAEventMaker\n",
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"from lsst.summit.utils.efdUtils import EfdClient, getEfdData, makeEfdClient, getDayObsEndTime, getDayObsStartTime\n",
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"from lsst.ts.xml.tables.m1m3 import FATable"
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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": null,
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"id": "3a0d6e99-ab66-45ab-b7b3-49b2ffbcdfc0",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create an EFD client instance\n",
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"client = makeEfdClient()\n",
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"\n",
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"# Define the start and end time\n",
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"t_start = Time(t_start)\n",
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"t_end = Time(t_end)\n",
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"\n",
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"main(t_start, t_end)\n"
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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": null,
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"id": "544289e3-35b4-40fd-9311-f510cc89efa4",
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"metadata": {},
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"outputs": [],
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"source": [
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"def loop_over_actuators(client, topic, nb_actuators, t_start, t_end):\n",
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" print(f\"Processing {topic}\")\n",
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"\n",
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" plot_directory = \"./plots/\"\n",
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" if not os.path.exists(plot_directory):\n",
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" os.makedirs(plot_directory)\n",
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" FA_error = [f\"{topic}{i}\" for i in range(nb_actuators)]\n",
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"\n",
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" if \"secondary\" in topic:\n",
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" secondary_actuator_id = np.array([])\n",
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" for i in range(len(FATable)):\n",
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" if FATable[i].s_index is not None:\n",
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" secondary_actuator_id = np.append(\n",
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" secondary_actuator_id, FATable[i].actuator_id\n",
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" )\n",
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" secondary_actuator_id = secondary_actuator_id.astype(int)\n",
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" \n",
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" for fa in range(nb_actuators):\n",
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" if fa % 10 == 0:\n",
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" print(f\"{fa}/{nb_actuators}\")\n",
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" df = getEfdData(\n",
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" client,\n",
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" \"lsst.sal.MTM1M3.forceActuatorData\",\n",
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" columns=FA_error,\n",
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" begin=t_start,\n",
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" end=t_end,\n",
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" )\n",
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" \n",
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" dt = (df[f\"{topic}0\"].index[1] - df[f\"{topic}0\"].index[0]).total_seconds()\n",
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" freqs = np.fft.fftfreq(len(df), d=dt)\n",
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" positive_mask = freqs > 0\n",
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" fft_frequency = freqs[positive_mask]\n",
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" fft_result = np.fft.fft(df.values, axis=0)\n",
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" fft_magnitudes = np.abs(fft_result[positive_mask, :])\n",
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" plt.figure(figsize=(10, 5))\n",
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" \n",
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" if \"primary\" in topic:\n",
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" label = FATable[fa].actuator_id\n",
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" else:\n",
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" label = secondary_actuator_id[fa]\n",
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" \n",
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" plt.plot(\n",
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" fft_frequency, fft_magnitudes[:, fa], label=f\"Primary actuator {label}\"\n",
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" )\n",
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" plt.title(f\"Power spectrum from {t_start} to {t_end}\")\n",
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" plt.xlabel(\"Frequency [Hz]\")\n",
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" plt.ylabel(\"Magnitude\")\n",
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" plt.legend()\n",
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" plt.show()\n",
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"\n",
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" #if \"primary\" in topic:\n",
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" # plt.savefig(f\"{plot_directory}PA_{FATable[fa].actuator_id}.png\")\n",
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" #else:\n",
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" # plt.savefig(f\"{plot_directory}SA_{secondary_actuator_id[fa]}.png\")\n",
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"\n",
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" \n",
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"def main(t_start, t_end):\n",
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"\n",
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" loop_over_actuators(\n",
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" client, \"primaryCylinderFollowingError\", len(FATable), t_start, t_end\n",
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" )\n",
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"\n",
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" loop_over_actuators(\n",
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" client, \"secondaryCylinderFollowingError\", 112, t_start, t_end\n",
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" )\n",
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" "
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "LSST",
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"language": "python",
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"name": "lsst"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.10"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎sst/mtm1m3/M1M3_Force_actuator_following_errors_FFT.yaml

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title: M1M3 Force Actuator following errors FFT
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description: Compute of FFT (Fast Fourier Transform) of the M1M3 force actuator following error signals over a specified time range
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authors:
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- name: Nacho Sevilla Noarbe y Laura Toribio San Cipriano
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slack: nsevilla and laura.toribio
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tags:
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- m1m3
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- force actuator
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- error analysis
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parameters:
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t_start:
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type: string
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description: Starting time to plot the bump tests
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default: "2025-05-27T18:30:15"
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t_end:
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type: string
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description: Ending time to plot the bump tests
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default: "2025-05-27T18:30:24"

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