SITCOMTN-105
Symonyi Telescope - Post-Balancing Data Analysis#
Abstract
We are getting ready to balance the telescope twice in the next weeks. First, we will balance the telescope with ComCam and M2 Glass. The M2 Glass and M2 Surrogate have similar weights, with a small difference. We expect the torques applied by the elevation drives will be very close to the previous balancing event(s). A couple of weeks later, we will repeat the procedure with ComCam, M2 Glass, and M1M3 Glass. The M1M3 Glass and M1M3 Cell assembly is much heavier than the M1M3 Mass Simulator (yellow cross) and hundreds of kilograms heavier than the M1M3 Surrogate and M1M3 Cell configuration. This procedure will be much more delicate due to the size and mass of the mirror.
We want to establish a baseline before we start the procedure, and we need someone to review the data to determine whether we can proceed quickly.
The links below point to old night logs that might contain useful information. Feel free to unlink them if they are not useful.
Here is an approximate timeline of different integration phases where we needed to re-balance the telescope. We do not necessarily need the whole process. We need the torques once the telescope is already balanced as a baseline.
May to Aug 2023 - M1M3 Surrogate and M1M3 Cell on the TMA
Nov 2023 to Jan 2024 - M1M3 Surrogate and Cell, M2 Surrogate and Cell on the TMA
Feb to Apr 2024 - M2 Surrogate and Cell on the TMA
Set up notebook#
Let’s start importing a few libraries to use in this notebook.
# Notebook extensions for formatting and auto-reload libraries
%matplotlib inline
%load_ext lab_black
%load_ext autoreload
%autoreload 2
# Standard Python Libraries
import os
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import numpy as np
import pandas as pd
from astropy.time import Time
from collections import defaultdict
from datetime import datetime, timedelta
from scipy.stats import norm
# LSST Specific Libraries
from lsst_efd_client import EfdClient
from lsst.summit.utils.blockUtils import BlockParser
from lsst.summit.utils.efdUtils import makeEfdClient, getEfdData, getDayObsForTime
from lsst.summit.utils.tmaUtils import (
getCommandsDuringEvent,
TMAEvent,
TMAEventMaker,
TMAState,
)
The lab_black extension is already loaded. To reload it, use:
%reload_ext lab_black
The autoreload extension is already loaded. To reload it, use:
%reload_ext autoreload
Setting up the sub-directories#
# Set-up paths
plot_path = "plots"
data_path = "data"
os.makedirs(plot_path, exist_ok=True)
os.makedirs(data_path, exist_ok=True)
# Enable verbose mode?
verbose = False
topic_az = "lsst.sal.MTMount.azimuth"
fields_az = ["actualTorque"]
topic_el = "lsst.sal.MTMount.elevation"
fields_el = ["actualPosition"]
Helper Functions#
When this notebook gets into a more mature state, we will want to move the code below to a python library so the it gets cleaner.
async def query_bump_logs_in_chunks(
start_date,
end_date,
client_name="",
chunk_size_days=3,
topic_name="lsst.sal.MTM1M3.logevent_logMessage",
fields=["message"],
):
"""
Queries the log messages related to bump tests from the EFD in chunks.
Args:
start_date (str): Start date of the query in ISO format (YYYY-MM-DD).
end_date (str): End date of the query in ISO format (YYYY-MM-DD).
client_name (str, optional): Name of the EFD client. Defaults to "".
chunk_size_days (int, optional): Number of days per chunk. Defaults to 3.
topic_name (str, optional): SAL topic name to be queried by the client. Defaults to lsst.sal.MTM1M3.logevent_logMessage.
fields (list[str], optional): Fields to be queried by the client. Defaults to ["message"].
Returns:
pandas.DataFrame: Concatenated DataFrame containing the queried log messages.
"""
client = makeClient(client_name)
# Convert start and end dates to datetime objects
start = datetime.fromisoformat(start_date)
end = datetime.fromisoformat(end_date)
# Initialize an empty DataFrame to store concatenated results
all_data = pd.DataFrame()
current_start = start
while current_start < end:
current_end = min(current_start + timedelta(days=chunk_size_days), end)
try:
# Query the data for the current chunk
chunk_data = await client.select_time_series(
topic_name=topic_name,
fields=fields,
start=Time(current_start.isoformat(), format="isot", scale="utc"),
end=Time(current_end.isoformat(), format="isot", scale="utc"),
)
# Concatenate the chunk data to the main DataFrame
all_data = pd.concat([all_data, chunk_data], ignore_index=False)
except Exception as e:
print(
f"Error querying data from {current_start.isoformat()} to {current_end.isoformat()}: {e}"
)
continue # Optionally, continue to the next chunk
# Move to the next chunk
current_start = current_end
return all_data
def makeClient(client_name):
# Create the client based on client_name
if client_name == "summit_efd":
return makeEfdClient("summit_efd")
elif client_name == "usdf_efd":
return makeEfdClient("usdf_efd")
elif client_name == "idf_efd":
return makeEfdClient("idf_efd")
else:
return makeEfdClient() # Default client
# Example usage:
# begin = "2023-11-13T01:00"
# end = "2023-12-21T01:00"
# bump_logs = await query_bump_logs_in_chunks(begin, end, client_name='')
def showAndClear():
plt.show()
# Clear the current axes.
plt.cla()
# Clear the current figure.
plt.clf()
# Closes all the figure windows.
plt.close("all")
plt.close(fig)
return
async def getDataFrame(client, starts, ends, topic, verbose=True, fields=None):
all_data = pd.DataFrame()
for start, end in zip(starts, ends):
if verbose:
print(
r"Starting query for time range {} - {}".format(start, end),
end=" . . . ",
)
if fields != None:
df_bump = await client.select_time_series(
topic, fields, Time(start), Time(end)
)
else:
df_bump = await client.select_time_series(
topic, "*", Time(start), Time(end)
)
all_data = pd.concat([all_data, df_bump], ignore_index=False)
del df_bump
if verbose:
print("Finished")
return all_data
def makeDateRange(startPoint, endPoint, step=np.timedelta64(1, "D")):
starts = np.arange(startPoint, endPoint, step=step)
ends = starts + np.timedelta64(1, "D")
return starts, ends
def fitGaussian(data, ax):
mu, std = norm.fit(data)
xmin, xmax = ax.get_xlim()
x = np.linspace(np.floor(xmin), np.ceil(xmax), int(10e4))
p = norm.pdf(x, mu, std)
return mu, std, p, x, xmin, xmax
def getFWHM_from_gaussian(sigma):
return 2 * np.sqrt(np.log(2) * 2) * sigma
M1M3 Surrogate and M1M3 Cell on the TMA - May 2023-Aug 2023#
first_day = "2023-05-25"
last_day = "2023-06-25"
date_range = pd.date_range(first_day, last_day, freq="1D")
date_range
DatetimeIndex(['2023-05-25', '2023-05-26', '2023-05-27', '2023-05-28',
'2023-05-29', '2023-05-30', '2023-05-31', '2023-06-01',
'2023-06-02', '2023-06-03', '2023-06-04', '2023-06-05',
'2023-06-06', '2023-06-07', '2023-06-08', '2023-06-09',
'2023-06-10', '2023-06-11', '2023-06-12', '2023-06-13',
'2023-06-14', '2023-06-15', '2023-06-16', '2023-06-17',
'2023-06-18', '2023-06-19', '2023-06-20', '2023-06-21',
'2023-06-22', '2023-06-23', '2023-06-24', '2023-06-25'],
dtype='datetime64[ns]', freq='D')
eventMaker = TMAEventMaker()
un_count, stopcount, slewcount = 0, 0, 0
slew_events = []
unit_events = []
stop_events = []
for date in date_range:
dayObs = getDayObsForTime(Time(date))
events = eventMaker.getEvents(dayObs)
_slew_events = [
evt for evt in events if (evt.type in [TMAState.SLEWING, TMAState.TRACKING])
]
_unit_events = [evt for evt in events if (evt.type == TMAState.UNINITIALIZED)]
_stop_events = [evt for evt in events if (evt.type == TMAState.STOPPED)]
slew_events.extend(_slew_events)
unit_events.extend(_unit_events)
stop_events.extend(_stop_events)
print(f"Number of uninitialized blocks: {len(unit_events)}")
print(f"Number of slew blocks: {len(slew_events)}")
print(f"Number of stopped blocks: {len(stop_events)}")
new_start_array = [evt.begin for evt in slew_events]
new_end_array = [evt.end for evt in slew_events]
lsst.summit.utils.tmaUtils INFO: Retrieving mount data for 20230524 from the EFD
lsst.summit.utils.tmaUtils WARNING: No EFD data found for dayObs=20230524
lsst.summit.utils.tmaUtils INFO: Retrieving mount data for 20230525 from the EFD
lsst.summit.utils.tmaUtils INFO: Retrieving mount data for 20230526 from the EFD
lsst.summit.utils.tmaUtils INFO: Retrieving mount data for 20230527 from the EFD
lsst.summit.utils.tmaUtils WARNING: No EFD data found for dayObs=20230527
lsst.summit.utils.tmaUtils INFO: Retrieving mount data for 20230528 from the EFD
lsst.summit.utils.tmaUtils WARNING: No EFD data found for dayObs=20230528
lsst.summit.utils.tmaUtils INFO: Retrieving mount data for 20230529 from the EFD
lsst.summit.utils.tmaUtils INFO: Retrieving mount data for 20230530 from the EFD
lsst.summit.utils.tmaUtils INFO: Retrieving mount data for 20230531 from the EFD
lsst.summit.utils.tmaUtils INFO: Retrieving mount data for 20230601 from the EFD
lsst.summit.utils.tmaUtils INFO: Retrieving mount data for 20230602 from the EFD
lsst.summit.utils.tmaUtils WARNING: No EFD data found for dayObs=20230602
lsst.summit.utils.tmaUtils INFO: Retrieving mount data for 20230603 from the EFD
lsst.summit.utils.tmaUtils WARNING: No EFD data found for dayObs=20230603
lsst.summit.utils.tmaUtils INFO: Retrieving mount data for 20230604 from the EFD
lsst.summit.utils.tmaUtils WARNING: No EFD data found for dayObs=20230604
lsst.summit.utils.tmaUtils INFO: Retrieving mount data for 20230605 from the EFD
lsst.summit.utils.tmaUtils WARNING: No EFD data found for dayObs=20230605
lsst.summit.utils.tmaUtils INFO: Retrieving mount data for 20230606 from the EFD
lsst.summit.utils.tmaUtils WARNING: No EFD data found for dayObs=20230606
lsst.summit.utils.tmaUtils INFO: Retrieving mount data for 20230607 from the EFD
lsst.summit.utils.tmaUtils WARNING: No EFD data found for dayObs=20230607
lsst.summit.utils.tmaUtils INFO: Retrieving mount data for 20230608 from the EFD
lsst.summit.utils.tmaUtils WARNING: No EFD data found for dayObs=20230608
lsst.summit.utils.tmaUtils INFO: Retrieving mount data for 20230609 from the EFD
lsst.summit.utils.tmaUtils WARNING: No EFD data found for dayObs=20230609
lsst.summit.utils.tmaUtils INFO: Retrieving mount data for 20230610 from the EFD
lsst.summit.utils.tmaUtils WARNING: No EFD data found for dayObs=20230610
lsst.summit.utils.tmaUtils INFO: Retrieving mount data for 20230611 from the EFD
lsst.summit.utils.tmaUtils WARNING: No EFD data found for dayObs=20230611
lsst.summit.utils.tmaUtils INFO: Retrieving mount data for 20230612 from the EFD
lsst.summit.utils.tmaUtils WARNING: No EFD data found for dayObs=20230612
lsst.summit.utils.tmaUtils INFO: Retrieving mount data for 20230613 from the EFD
lsst.summit.utils.tmaUtils WARNING: No EFD data found for dayObs=20230613
lsst.summit.utils.tmaUtils INFO: Retrieving mount data for 20230614 from the EFD
lsst.summit.utils.tmaUtils WARNING: No EFD data found for dayObs=20230614
lsst.summit.utils.tmaUtils INFO: Retrieving mount data for 20230615 from the EFD
lsst.summit.utils.tmaUtils INFO: Retrieving mount data for 20230616 from the EFD
lsst.summit.utils.tmaUtils WARNING: No EFD data found for dayObs=20230616
lsst.summit.utils.tmaUtils INFO: Retrieving mount data for 20230617 from the EFD
lsst.summit.utils.tmaUtils WARNING: No EFD data found for dayObs=20230617
lsst.summit.utils.tmaUtils INFO: Retrieving mount data for 20230618 from the EFD
lsst.summit.utils.tmaUtils WARNING: No EFD data found for dayObs=20230618
lsst.summit.utils.tmaUtils INFO: Retrieving mount data for 20230619 from the EFD
lsst.summit.utils.tmaUtils INFO: Retrieving mount data for 20230620 from the EFD
lsst.summit.utils.tmaUtils INFO: Retrieving mount data for 20230621 from the EFD
lsst.summit.utils.tmaUtils INFO: Retrieving mount data for 20230622 from the EFD
lsst.summit.utils.tmaUtils INFO: Retrieving mount data for 20230623 from the EFD
lsst.summit.utils.tmaUtils INFO: Retrieving mount data for 20230624 from the EFD
lsst.summit.utils.tmaUtils WARNING: No EFD data found for dayObs=20230624
Number of uninitialized blocks: 0
Number of slew blocks: 950
Number of stopped blocks: 0
all_data_az = await getDataFrame(
client, new_start_array, new_end_array, topic_az, fields=fields_az, verbose=verbose
)
# all_data_el = await getDataFrame(client,new_start_array,new_end_array,topic_el,fields=fields_el,verbose=verbatim)
%matplotlib inline
figure_name = "balancing_m1m3surr_May23_Aug23"
fig, axs = plt.subplots(1, 2, figsize=[10, 6], num=figure_name)
axs[0].scatter(all_data_az.index, all_data_az)
axs[0].set_ylabel("Torque [N m]")
axs[0].set_xticks(
np.arange(
np.min(all_data_az.index.tz_localize(None)),
np.max(all_data_az.index.tz_localize(None)),
step=np.timedelta64(5, "D"),
)
)
axs[0].set_xlim(
np.min(all_data_az.index.tz_localize(None)),
np.max(all_data_az.index.tz_localize(None)),
)
axs[1].hist(
all_data_az["actualTorque"],
bins=50,
facecolor="#2ab0ff",
edgecolor="#169acf",
linewidth=0.5,
)
mu, std, p, x, xmin, xmax = fitGaussian(all_data_az["actualTorque"], axs[1])
axs[1].plot(
x,
p,
label="$\mu$={:.2E}, $\sigma$={:.2E}".format(mu, std),
linewidth=2,
color="red",
)
axs[1].set_ylabel("Counts")
axs[1].set_xlabel("Torque [N m]")
axs[1].legend()
axs[1].set_xlim(xmin, xmax)
for ax in axs:
ax.grid()
fig.tight_layout()
fig.savefig(f"{figure_dir}/{figure_name}.jpg", dpi=200)
plt.show()
# showAndClear()
M1M3 Surrogate and Cell, M2 Surrogate and Cell on the TMA - Nov 2023 to Jan 2024#
startPoint = np.datetime64("2023-11-01T12:00:00")
endPoint = np.datetime64("2024-01-01T12:00:00")
starts, ends = makeDateRange(startPoint, endPoint)
all_data = await getDataFrame(client,starts,ends,topic,fields=fields,verbose=False)
lolim,uplim = pd.Timestamp("2023-11-14").tz_localize("UTC"),pd.Timestamp("2023-11-25").tz_localize("UTC")
limited_data = all_data.loc[lolim:uplim]
fig,axs = plt.subplots(2,1,figsize=[10,6],sharex=True)
for ax in axs:
ax.plot(limited_data.index,limited_data)
ax.set_ylabel("Torque [N m]")
ax.set_xticks(np.arange(lolim,uplim,step=np.timedelta64(1,"D")))
ax.set_xlim(lolim,uplim)
ax.grid()
axs[0].set_ylim(-0.4E5,0.4E5)
axs[1].set_ylim(-0.8E4,0.8E4)
axs[1].set_xlabel("Date")
showAndClear()
M2 Surrogate and Cell on the TMA - Feb to Apr 2024#
startPoint = np.datetime64("2024-02-01T12:00:00")
endPoint = np.datetime64("2024-04-01T12:00:00")
starts, ends = makeDateRange(startPoint, endPoint)
all_data = await getDataFrame(client,starts,ends,topic,fields=fields,verbose=False)
lolim,uplim = pd.Timestamp("2024-02-05").tz_localize("UTC"),pd.Timestamp("2024-02-15").tz_localize("UTC")
limited_data = all_data.loc[lolim:uplim]
fig,axs = plt.subplots(2,1,figsize=[10,6],sharex=True)
for ax in axs:
ax.plot(limited_data.index,limited_data)
ax.set_ylabel("Torque [N m]")
ax.set_xticks(np.arange(lolim,uplim,step=np.timedelta64(2,"D")))
ax.set_xlim(lolim,uplim)
ax.grid()
axs[0].set_ylim(-2E5,2E5)
axs[1].set_ylim(-0.8E2,0.8E2)
axs[1].set_xlabel("Date")
showAndClear()