Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

SITCOM-918: oscillations in m1m3 #105

Draft
wants to merge 8 commits into
base: develop
Choose a base branch
from
Draft

SITCOM-918: oscillations in m1m3 #105

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
8 commits
Select commit Hold shift + click to select a range
ff2aadb
identification script
psferguson Nov 10, 2023
d2a25ed
placeholder notebook
psferguson Nov 13, 2023
2e68096
squash
psferguson Nov 21, 2023
9c02888
plots for glass safety
psferguson Dec 12, 2023
b4af2e8
updates
psferguson Mar 26, 2024
7794aa6
draft of identification notebook
psferguson Mar 28, 2024
5f8517d
remove notebook
psferguson Mar 28, 2024
a39d400
cleaning up
psferguson Mar 28, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
336 changes: 336 additions & 0 deletions notebooks/tel_and_site/subsys_req_ver/m1m3/SITCOMTN_81_20231128_topple_block_slews.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,336 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "34c30bfe-0ecc-45ad-977b-eff34fa3fdf4",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from glob import glob\n",
"import sys, time, os, asyncio\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import numpy as np\n",
"from scipy.signal import find_peaks\n",
"from scipy.interpolate import interp1d\n",
"from astropy.time import Time, TimeDelta\n",
"from scipy.interpolate import UnivariateSpline\n",
"\n",
"\n",
"from lsst_efd_client import EfdClient\n",
"from lsst.ts.idl.enums import MTM1M3\n",
"\n",
"from lsst.summit.utils.efdUtils import calcNextDay, getEfdData\n",
"\n",
"import lsst.sitcom.vandv.m1m3.sitcomtn81.sitcomtn_81_identify_oscillations as sitcomtn81\n",
"\n",
"from tqdm import tqdm\n",
"\n",
"import logging\n",
"\n",
"summit_utils_logger = logging.getLogger(\"lsst.summit.utils\")\n",
"summit_utils_logger.setLevel(logging.ERROR)\n",
"\n",
"%matplotlib inline\n",
"%load_ext lab_black\n",
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "markdown",
"id": "d34a3dd1-3a9c-48b6-b042-8997384e9bf3",
"metadata": {},
"source": [
"# Characterizaiton of topple block \n",
"\n",
"\n",
"Timestamps of the topple block slews:\n",
"Dayobs, seqnum, eventnum, time (utc)\n",
"20231129, 73, 13, 1701269673.9407606\n",
"20231129, 90, 30, 1701298313.7859175\n",
"20231129, 97, 33, 1701299609.625537\n",
"20231129, 99, 38, 1701299684.265059\n",
"20231129, 100, 49, 1701299719.8852365\n",
"20231129, 100, 54, 1701299775.7053618\n",
"20231129, 107, 61, 1701302257.7229214\n",
"20231129, 114, 63, 1701303679.5524187\n",
"20231129, 120, 70, 1701305359.8571882\n",
"20231129, 365, 79, 1701316217.89929\n",
"20231129, 385, 80, 1701318835.4638815\n",
"20231129, 387, 81, 1701318835.4638815\n",
"20231129, 387, 82, 1701318878.6672893\n",
"20231129, 399, 84, 1701320922.0731387\n",
"20231129, 811, 101, 1701332665.7784734"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "55c9ab9a-9ccf-425e-99c9-d626abf1a973",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"def add_timestamp(data):\n",
" \"\"\"\n",
" Adds a correct timestamp column in UTC format to the provided data if\n",
" not present.\n",
"\n",
" Parameters\n",
" ----------\n",
" data : DataFrame\n",
" The data to which the timestamp will be added.\n",
"\n",
" Returns\n",
" -------\n",
" DataFrame\n",
" The data with the added 'snd_timestamp_utc' column.\n",
" \"\"\"\n",
" if \"snd_timestamp_utc\" not in data.columns:\n",
" data[\"snd_timestamp_utc\"] = Time(\n",
" data[\"private_sndStamp\"], format=\"unix_tai\"\n",
" ).unix\n",
" return data\n",
"\n",
"\n",
"def get_data(events_frame, client, train=False):\n",
" table_dict = {\n",
" \"m1m3_hp_actuator\": \"lsst.sal.MTM1M3.hardpointActuatorData\",\n",
" }\n",
" query_dict = {}\n",
" for key in table_dict.keys():\n",
" query_dict[key] = []\n",
" for j, time in tqdm(enumerate(events_frame[\"times\"])):\n",
" event_time = Time(events_frame[\"times\"][j] - 2, format=\"unix\")\n",
"\n",
" for key in table_dict.keys():\n",
" hpcols = [\"private_sndStamp\"] + [f\"measuredForce2\"] # for i in range(6)]\n",
" query = getEfdData(\n",
" client=client,\n",
" topic=table_dict[key],\n",
" columns=hpcols,\n",
" begin=event_time,\n",
" end=event_time,\n",
" prePadding=60,\n",
" postPadding=60,\n",
" )\n",
" query[\"event_num\"] = j\n",
" query[\"seqNum\"] = events_frame[\"seq_num\"][j]\n",
" query[\"event_time\"] = events_frame[\"times\"][j] + 3\n",
" query = add_timestamp(query)\n",
"\n",
" # query[\"block\"] = block\n",
" query[\"oscillation_bool\"] = 0\n",
" query[\"delta_time\"] = (\n",
" query[\"snd_timestamp_utc\"] - events_frame[\"times\"][j] + 3\n",
" )\n",
" if train:\n",
" sel = query[\"delta_time\"] >= -5\n",
" sel &= query[\"delta_time\"] < 1\n",
" query.loc[sel, \"oscillation_bool\"] = 1\n",
"\n",
" query_dict[key].append(query)\n",
" for key in table_dict.keys():\n",
" query_dict[key] = pd.concat(query_dict[key])\n",
" sorted_keys = sorted(query_dict.keys())\n",
" initial_key = sorted_keys[0]\n",
" merged_df = query_dict[initial_key].sort_index()\n",
" # Define your tolerance for matching\n",
" tolerance = 0.03 # pd.Timedelta('0.03 seconds')\n",
"\n",
" # Iterate over the remaining DataFrames and merge them\n",
" for key in sorted_keys[1:]:\n",
" merged_df = pd.merge_asof(\n",
" merged_df,\n",
" query_dict[key].sort_index(),\n",
" left_on=\"snd_timestamp_utc\",\n",
" right_on=\"snd_timestamp_utc\",\n",
" tolerance=tolerance,\n",
" direction=\"nearest\",\n",
" suffixes=(\"\", \"_\" + key),\n",
" )\n",
" merged_df.reset_index(drop=True, inplace=True)\n",
"\n",
" return merged_df"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "46b02dee-fd14-4646-a00b-dda499a7d033",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"days_with_events = [\n",
" i for i in days_with_events if (float(i) > 20231120) & (float(i) < 20231205)\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "00951deb-3242-4f4e-aa50-fd3549bcb39c",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"days_with_events"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2b4afdd4-f48a-40b8-af77-8d651b55d50a",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"\n",
"day = 20231129\n",
"eventMaker = TMAEventMaker()\n",
"events = eventMaker.getEvents(int(day))\n",
"\n",
"events_frame = pd.read_csv(f\"./sitcomtn81_data/oscillation_events_20231129.csv\")\n",
"merged_df = get_data(events_frame, client)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "87ded02e-e64c-4f1b-9657-f8f7b28300cb",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"good = [13, 30, 33, 38, 49, 54, 61, 63, 70, 79, 80, 81, 82, 84, 101]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "04e78cfa-aacd-4760-beb9-51ca34f82400",
"metadata": {},
"outputs": [],
"source": [
"offset_list = [10, 2, 3.5, 2, 1, 1.5, 2, 4, 9, 4, 1, 1, 8.5, 7.5, -21]\n",
"offset_dict = {}\n",
"for l, k in enumerate(good):\n",
" offset_dict[k] = offset_list[l]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "751e78df-9683-40b7-882c-f8342ae0d7c1",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"l = 1\n",
"plt.figure(dpi=175, figsize=(10, 8 * l))\n",
"j = 0\n",
"ptp_list = []\n",
"offset = 2000\n",
"for i in np.unique(merged_df[\"event_num\"]): # [k : k + 30 * l]:\n",
" if i not in good:\n",
" continue\n",
" plt.title(f\"{day} slews with oscillations\")\n",
" subframe = merged_df.copy()\n",
" subframe = subframe[subframe[\"event_num\"] == i]\n",
" seq_val = np.unique(subframe[\"seqNum\"])\n",
" # block_num = np.unique(subframe[\"block\"])\n",
" if len(subframe) > 0:\n",
" ptp_range = (subframe[\"delta_time\"] - offset_dict[i] > 0) & (\n",
" subframe[\"delta_time\"] - offset_dict[i] < 4\n",
" )\n",
"\n",
" plt.plot(\n",
" subframe[\"delta_time\"][ptp_range] - offset_dict[i],\n",
" subframe[\"measuredForce2\"][ptp_range]\n",
" - subframe[\"measuredForce2\"][subframe[\"delta_time\"] < 0].mean()\n",
" - offset * j,\n",
" label=f\"{seq_val[0]}\",\n",
" )\n",
" ptp_range = (subframe[\"delta_time\"] - offset_dict[i] > 0) & (\n",
" subframe[\"delta_time\"] - offset_dict[i] < 4\n",
" )\n",
" ptp_list.append(np.ptp(subframe[\"measuredForce2\"][ptp_range]))\n",
" j += 1\n",
"if l == 1:\n",
" plt.legend(title=\"slew number\")\n",
"if l == 4:\n",
" plt.legend(fontsize=9.2, loc=7)\n",
"# plt.axvline(0, c=\"k\", ls=\"dashed\")\n",
"plt.ylabel(f\"$\\Delta$ HP 2 force [N] - {offset} N offset\")\n",
"plt.xlabel(\"Time [s]\")\n",
"plt.xlim(-10, 25)\n",
"for l in np.arange(0, 5, 1):\n",
" plt.axvline(l, ls=\"dashed\", c=\"k\")\n",
"# plt.savefig(f\"./sitcomtn81_data/plots/osc_hp2_{day}.png\")\n",
"\n",
"# plt.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "540fbb3a-0f24-4ff8-9f82-770eea878a82",
"metadata": {},
"outputs": [],
"source": [
"print(np.mean(ptp_list), np.std(ptp_list))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "56037911-95c9-4a66-ba6b-3e1666999437",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"seq_list = []\n",
"for row in good:\n",
" day_obs, seq_num, times = events_frame.loc[row, [\"day_obs\", \"seq_num\", \"times\"]]\n",
" print(f\"{day_obs}, {seq_num}, {row}, {times}\")\n",
" seq_list.append(seq_num)\n",
" events_frame.loc[row, [\"day_obs\", \"seq_num\", \"times\"]].to_csv(\n",
" f\"{datapath}/cleaned_events{day}.csv\"\n",
" )"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "LSST",
"language": "python",
"name": "lsst"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Loading