102. Rubin Schedule Viewer#

102_rubin_schedule_viewer

102. Rubin Schedule Viewer¶

For the Rubin Science Platform at data.lsst.cloud.
Container Size: Large
LSST Science Pipelines version: v29.2.0
Last verified to run: 2026-02-18
Repository: github.com/lsst/tutorial-notebooks
DOI: 10.11578/rubin/dc.20250909.20

Learning objective: How to programmatically query the Rubin Schedule Viewer.

Rubin service: The Rubin Schedule Viewer service runs at the USDF and is publicly available at Rubin Schedule Viewer.

Packages: requests

Credit: Developed by the Rubin Community Science team. Please consider acknowledging them if this notebook is used for the preparation of journal articles, software releases, or other notebooks.

Get Support: Everyone is encouraged to ask questions or raise issues in the Support Category of the Rubin Community Forum. Rubin staff will respond to all questions posted there.

1. Introduction¶

This tutorial demonstrates how to query the Rubin Schedule Viewer, which is currently openly available via the US Data Facility at https://usdf-rsp.slac.stanford.edu/obsloctap/static/viewer.html (reload the page if it appears blank, and give it time to execute the default query).

The Rubin Schedule Viewer service publishes both the next scheduled visit and the predicted observing schedule for Rubin Observatory. It provides a user-friendly web interface as well as a programmatic web API, enabling the community to access up-to-date information for planning observations. The Rubin Schedule Viewer provides a nominal survey schedule at least 24 hours in advance, followed by updated schedules published at least 2 hours before each observing visit.

The design of the service is described in DMTN-263.

1.1. ObsLocTAP - Observation Locator Table Access Protocol¶

The Rubin Schedule Viewer is built using the IVOA Observation Locator Table Access Protocol (ObsLocTAP henceforth) standard (Salgado et al.) and incorporates information from an observatory's scheduler, including both forecasted observations and the historical record of completed visits.

ObsLocTAP specifies - in a standard format - services to retrieve information about planned, scheduled and performed observations of a given target (or coordinates) for a given astronomical observatory based on the existing ObsCore data model. This standard does not describe the access to data obtained after the processing of the observational activity, as that is the goal of ObsCore (archived observations), although the discovery could be done in a similar way.

ObsLocTAP defines a data model for scheduled observations and a method to run queries over compliant data, using several Virtual Observatory technologies.

1.2. Key columns¶

The ObsLocTAP data model (table schema) for the scheduled observations is defined in the ivoa.obsplan table -- Observation Locator Table Access Protocol Version 1.0

The database contains 28 columns in total, and these are the key columns.

t_min : The start time (MJD).
t_max : The stop time (MJD).
s_ra : The Right Ascension of the visit or planned observation (deg).
s_dec : The Declination of the visit or planned observation (deg).
em_min : The filter minimum wavelength (m).
em_max : The filter maximum wavelength (m).
target_name : The name of the astronomical object observed, if any.
tracking_type : One of the following values: Sidereal, Solar-system-object-tracking, Fixed-az-el-transit.
priority : The Priority level { 0, 1, 2}. 0 = in the target queue as the next visit (meaning its execution is highly likely), 1 = in the 1 hour look-ahead summit queue (meaning likely), 2 = 24 hour pre-night simulation, meaning it is not in the summit queue and has a high degree of uncertainty.
t_planning : Time (MJD) when this observation has been added or modified into the planning log.
execution_status : Execution status of the prediction: One of the following values: Planned, Scheduled, Unscheduled, Performed, Aborted.

Key columns that remained unpopulated as of the last update of this tutorial.

s_region : Sky region covered by the data product (expressed in ICRS frame).
obs_id : The observation ID from the Camera (currently the same as target_name).

These are also columns of interest, but are generally the same for most observations taken with LSSTCam.

t_plan_exptime : The planned or scheduled exposure time (s).
t_exptime : The total exposure time = t_max - t_min (s).
s_fov : The diameter (bounds) of the covered region (deg).
s_resolution : The spatial resolution of the data expressed as FWHM (arcsec/pixel).
facility_name : Name of the facility used for this observation e.g Rubin Simonyi, AuxTel.
instrument_name : Name of the instrument used for this observation e.g LSSTCam, LSSTComCam.

See DMTN-263 for a description of all fields in the schema.

1.3. Import packages¶

Import the requests package to query the service and astropy's Time package to easily handle times and bokeh + holoviews for interactive visualization

In [1]:

import requests
import pandas as pd
import numpy as np
from astropy.time import Time

import holoviews as hv
from holoviews import opts
from bokeh.models import HoverTool
from bokeh.palettes import Turbo

import warnings

In [2]:

hv.extension("bokeh")
warnings.filterwarnings("ignore")

ⓘ

1.4. Define functions¶

Define a function to convert the em_min and em_max columns into the bandpass (filter) name.

In [3]:

def em_min_max_to_band(em_min, em_max):
    """
    Convert the wavelength minimum and maximum to band name.

    Parameters
    ----------
    em_min: float
        Wavelength minimum, in m.
    em_max: float
        Wavelength maximum, in m.

    Returns
    -------
    band: string
        Band (filter) name.
    """

    band = None
    band_dict = {'u': (2.95e-7, 4.05e-7), 'g': (4.00e-7, 5.55e-7),
                 'r': (5.50e-7, 6.92e-7), 'i': (6.90e-7, 8.20e-7),
                 'z': (8.15e-7, 9.25e-7), 'y': (9.20e-7, 11.1e-7)}
    for b in band_dict.keys():
        if em_min > band_dict[b][0] and em_max < band_dict[b][1]:
            band = b
    if band is None:
        print(em_min, em_max, ' no band found')

    return band

2. Rubin Schedule Viewer¶

The Rubin Schedule Viewer runs at the US Data Facility at SLAC.

Define the ObsLocTAP URL of the service.

In [4]:

obsloctap_url = "https://usdf-rsp.slac.stanford.edu/obsloctap"

2.1. Graphical User Interface¶

The Rubin Schedule Viewer User Interface (UI) provides a convenient graphical user interface to query and explore the Rubin schedule of observations. It provides the facility to interactively query the historical and future schedule from a specified point in time (MJD or ISO) for a number of hours into the future. It provides a filter by execution status, e.g. executed or scheduled, and a filter by target_name, e.g low-dust, LMC_SMC, dusty_plane.

The publicly accessible user interface is: https://usdf-rsp.slac.stanford.edu/obsloctap/static/viewer.html .

Programmatically check the health of the static UI by executing the following cell, and assert that the static user interface service must be alive.

Warning: If RemoteDisconnect errors are returned, it means the service is temporarily unstable or unavailable, and to try again later.

In [5]:

rubin_static_schedule_url = obsloctap_url + "/static/viewer.html"
response = requests.get(rubin_static_schedule_url)
assert response.status_code == 200, f"request failed with status {response.status_code}"
print(f"Rubin Schedule Viewer at {response.url} is alive.")

Rubin Schedule Viewer at https://usdf-rsp.slac.stanford.edu/obsloctap/static/viewer.html is alive.

2.2. Programmatic access¶

The service is also accessible programmatically, via ObsLocTAP, allowing users to extract metadata from the Schedule Viewer itself, including links such as the GitHub repository for the service code. The Schedule Viewer does not require authentication, which is essential for publicly distributing the observing schedule.

Connect to the service file using requests package and assert that the service is alive.

In [6]:

response = requests.get(obsloctap_url)
assert response.status_code == 200, f"request failed with status {response.status_code}"
print(f"Rubin Schedule Viewer API at {response.url} is alive.")

Rubin Schedule Viewer API at https://usdf-rsp.slac.stanford.edu/obsloctap/ is alive.

2.3. Query the service header information¶

Retrieve the header metadata and print the keys.

In [7]:

headers = response.json()
assert headers is not None
meta_data = headers["metadata"]
print(meta_data.keys())

dict_keys(['name', 'version', 'description', 'repository_url', 'documentation_url'])

Print the service's description.

In [8]:

print(
    f'ObsLocTap service running at USDF for "{meta_data.get("description")}" \
with documentation at  url {meta_data.get("documentation_url")}.'
)

ObsLocTap service running at USDF for "Look at scheduler forecast and track what was observed" with documentation at  url https://obsloctap.lsst.io.

3. Retrieve the future schedule¶

Query the Rubin Schedule Service to get the upcoming forecast for up to 24 hours in advance.

Define the schedule URL.

In [9]:

schedule_url = obsloctap_url + "/schedule"

Assert that the schedule forecast service is alive.

In [10]:

response = requests.get(schedule_url)
assert response.status_code == 200, f"request failed with status {response.status_code}"
print(f"Rubin Schedule Forecast  at {response.url} is alive.")

Rubin Schedule Forecast  at https://usdf-rsp.slac.stanford.edu/obsloctap/schedule is alive.

3.1. Retrieve the next 24 hours of schedule¶

As the default response of the schedule forecast is to return the next 24 hours of schedule, no additional parameters are needed.

Extract the list of upcoming visits as a json object and convert to a pandas dataframe, then display the first five rows.

In [11]:

forward_24h_schedule = pd.DataFrame(response.json())
print(len(forward_24h_schedule))

Print the first five rows.

In [12]:

forward_24h_schedule.head(5)

Out[12]:

	t_planning	target_name	obs_id	s_ra	s_dec	s_fov	s_resolution	t_min	...	facility_name	instrument_name	t_plan_exptime	category	priority	execution_status	tracking_type	rubin_nexp
0	61092.398175	lowdust	lowdust	188.206302	0.681197	3.0	0.2	61092.391230	...	Rubin:Simonyi	LSSTCam	30.0	Fixed	2	Scheduled	Sidereal	1
1	61092.398015	lowdust	lowdust	195.841827	-20.569343	3.0	0.2	61092.391071	...	Rubin:Simonyi	LSSTCam	30.0	Fixed	2	Not Observed	Sidereal	1
2	61092.397918	lowdust	lowdust	189.418299	5.462315	3.0	0.2	61092.390974	...	Rubin:Simonyi	LSSTCam	30.0	Fixed	2	Not Observed	Sidereal	1
3	61092.397918	lowdust	lowdust	189.418299	5.462315	3.0	0.2	61092.390974	...	Rubin:Simonyi	LSSTCam	30.0	Fixed	2	Not Observed	Sidereal	1
4	61092.397752	lowdust	lowdust	185.059095	1.222298	3.0	0.2	61092.390807	...	Rubin:Simonyi	LSSTCam	30.0	Fixed	2	Scheduled	Sidereal	1

5 rows × 28 columns

Note that the values in the "Execution Status" field are all "Scheduled".

3.1.1. Planned DDF observations¶

When alerts begin streaming in early 2026, template images will only exist for the DDFs, and thus alerts will only stream in the DDFs.

Print the number of planned observations per DDF in the next 24h, and in which filters the observations are planned.

First, add a column for the band (filter), converted from the em_min and em_max columns.

In [13]:

em_min = forward_24h_schedule['em_min'].values
em_max = forward_24h_schedule['em_max'].values
temp = []
for i in range(len(forward_24h_schedule)):
    temp.append(em_min_max_to_band(em_min[i], em_max[i]))
forward_24h_schedule['band'] = temp
del temp

Define the names of the DDF fields, in the format of the target_name column. For each field, sum the number of planned observations and print the unique filters for those observations.

In [14]:

ddf_names = ['ddf_cosmos', 'ddf_ecdfs', 'ddf_elaiss1',
             'ddf_edfs_a', 'ddf_edfs_b', 'ddf_xmm_lss']

for ddf_name in ddf_names:
    temp = forward_24h_schedule.query("target_name.str.contains(@ddf_name)")
    band_list = "".join(str(value) for value in np.unique(temp['band']))
    print('%-12s %3i  bands: %-15s' % (ddf_name, len(temp), band_list))
    del temp, band_list

ddf_cosmos   208  bands: girz
ddf_ecdfs      0  bands:
ddf_elaiss1    0  bands:
ddf_edfs_a    68  bands: girz
ddf_edfs_b    68  bands: girz
ddf_xmm_lss    0  bands:

3.2. Retrieve the next 2 hours of schedule¶

Define a parameter to only retrieve the next 2 hours of schedule.

In [15]:

params = {"time": "2"}
response = requests.get(schedule_url, params=params)
expected_url = f"{schedule_url}?time={params['time']}"

assert (response.url == expected_url)
assert response.status_code == 200, f"request failed with status {response.status_code}"

Retrieve the forecasted schedule as a pandas dataframe and print the number of forecasted visits.

In [16]:

next_visits = pd.DataFrame(response.json())
print(
    f"There are {len(next_visits)} visits scheduled in the next {params["time"]} hours."
)

There are 0 visits scheduled in the next 2 hours.

Option to display the forecasted visits.

In [17]:

# next_visits

4. Retrieve a past planned schedule¶

A start date can also be specified that is in the past to retrieve the observations that were planned on previous nights. The date can be MJD or ISO format.

Note that these are the observations that were scheduled for that night, they are not necessarily what was observed.

Define the start date as October 1 2025, and the time range as 24 hours.

In [18]:

params = {"time": "24", "start": "2025-10-01"}

In [19]:

response = requests.get(schedule_url, params=params)
assert response.status_code == 200, f"request failed with status {response.status_code}"
print(response.url)

https://usdf-rsp.slac.stanford.edu/obsloctap/schedule?time=24&start=2025-10-01

In [20]:

next_visits_from_date = response.json()
print(
    f"There were {len(next_visits_from_date)} visits \
scheduled in the {params["time"]} hours from {params['start']}."
)

There were 1298 visits scheduled in the 24 hours from 2025-10-01.

Times can also be defined with the astropy package's Time module.

Define past_time as October 29 2025, and print it as a Modified Julian Date (MJD).

In [21]:

past_time = (Time("2025-10-29 22:00:00", format="iso", scale="utc")).mjd
print(past_time)

60977.916666666664

In [22]:

params = {"time": "24", "start": past_time}

In [23]:

response = requests.get(schedule_url, params=params)
assert response.status_code == 200, f"request failed with status {response.status_code}"
print(response.url)

https://usdf-rsp.slac.stanford.edu/obsloctap/schedule?time=24&start=60977.916666666664

In [24]:

next_visits_from_date_iso = pd.DataFrame(response.json())
print(
    f"There were {len(next_visits_from_date_iso)} visits scheduled in the \
{params["time"]} hours from {Time(params['start'], format='mjd').iso}."
)

There were 712 visits scheduled in the 24 hours from 2025-10-29 22:00:00.000.

Display 5 of the visits of the retrieved table.

In [25]:

next_visits_from_date_iso.head(5)

Out[25]:

	t_planning	obs_id	obs_collection	s_ra	s_dec	s_fov	s_resolution	t_min	...	facility_name	instrument_name	t_plan_exptime	category	priority	execution_status	tracking_type	rubin_rot_sky_pos	rubin_nexp
0	60978.369741	2025082500179	BLOCK-T278_v2\|	33.707931	-42.486653	3.0	0.2	60913.158328	...	Rubin:Simonyi	LSSTCam	30.0	Fixed	2	Performed	Sidereal	26.530376	1
1	60978.369285	2025082500179	BLOCK-T278_v2\|	35.876726	-39.788507	3.0	0.2	60913.158328	...	Rubin:Simonyi	LSSTCam	30.0	Fixed	2	Performed	Sidereal	26.530376	1
2	60978.368845	2025082500179	BLOCK-T278_v2\|	39.465146	-39.939261	3.0	0.2	60913.158328	...	Rubin:Simonyi	LSSTCam	30.0	Fixed	2	Performed	Sidereal	26.530376	1
3	60978.368393	2025082500179	BLOCK-T278_v2\|	37.844238	-37.054514	3.0	0.2	60913.158328	...	Rubin:Simonyi	LSSTCam	30.0	Fixed	2	Performed	Sidereal	26.530376	1
4	60978.367953	2025082500179	BLOCK-T278_v2\|	41.304876	-37.152617	3.0	0.2	60913.158328	...	Rubin:Simonyi	LSSTCam	30.0	Fixed	2	Performed	Sidereal	26.530376	1

5 rows × 28 columns

4.1. Execution status¶

Note the values in the "Execution Status" field for the night of Oct 29 2025: they are all "Not Observed".

In [26]:

next_visits_from_date_iso["execution_status"].unique().tolist()

Out[26]:

['Performed', 'Not Observed']

During October 2025, the LSST has not started and testing at the summit is highly variable, and there was a very high probability that the observations scheduled were not executed.

This will change as the Rubin Observatory moves into steady state LSST survey operations.

5. Analyze a predicted schedule¶

Use the 24 hours of schedule prediction for Oct 29 2025, which was retrieved in Section 4.

5.1. Inter-visit time gaps¶

Extract the planned observation times and calculate the differences between successive values, in seconds.

In [27]:

t_planning = next_visits_from_date_iso["t_planning"].values
obs_visit_gap = []
for i in range(len(t_planning) - 1):
    diff = abs(t_planning[i] - t_planning[i + 1]) * 60 * 60 * 24
    if np.isnan(diff):
        continue
    obs_visit_gap.append(diff)
print(f"Number of differences: {len(obs_visit_gap)}")

Number of differences: 711

Print the minimum, maximum, and median visit gap time.

In [28]:

print(f"Min gap: {np.min(obs_visit_gap): .2f}")
print(f"Max gap: {np.max(obs_visit_gap): .2f}")
print(f"Mean gap: {np.mean(obs_visit_gap): .2f}")

Min gap:  34.07
Max gap:  326.45
Mean gap:  42.20

Create the hover tool.

In [29]:

hover = HoverTool(
    tooltips=[("Gap Range", "@left{0.00} - @right{0.00}"), ("Count", "@top")]
)

Create the histogram.

In [30]:

histogram = hv.Histogram(
    np.histogram(obs_visit_gap, bins=50), kdims="Time Gap", vdims="Counts"
).opts(
    title="Successive planned visit gaps for Oct 29 2025",
    xlabel="Gap (seconds)",
    ylabel="log(Count)",
    width=600,
    height=400,
    logy=True,
    line_color="steelblue",
    fill_color="lightblue",
    line_width=1,
    show_grid=True,
    tools=["hover", "pan", "wheel_zoom", "box_zoom", "reset"],
)
histogram

Out[30]:

Figure 1: The distribution of the log number of planned observations with a given time gap between it and the next observation.

5.2. Planned time by target field¶

Plot curves of the scheduled observation times for each target field that was planned to be observed the night of Oct 29 2025.

Get unique target names.

In [31]:

unique_targets = next_visits_from_date_iso["target_name"].unique()
target_to_y = {target: i for i, target in enumerate(unique_targets)}

Make a copy of the retrieved database of planned visits, and add the y-axis name to it.

In [32]:

df_plot = next_visits_from_date_iso.copy()
df_plot["y"] = df_plot["target_name"].map(target_to_y)

Create a color map from a palette with 30 colors.

In [33]:

np.random.seed(42)
palette = list(np.random.choice(Turbo[256], len(unique_targets), replace=False))
colors = {target: palette[i % len(palette)] for i, target in enumerate(unique_targets)}

Create the scatter plot for each target name, and overlay them.

In [34]:

scatter_plots = []
for target in unique_targets:
    target_data = df_plot[df_plot["target_name"] == target]
    scatter = hv.Scatter(
        target_data, kdims=["t_planning"], vdims=["y", "target_name"], label=target
    )
    scatter.opts(opts.Scatter(color=colors[target]))
    scatter_plots.append(scatter)

plot = hv.Overlay(scatter_plots)

Configure the hover tool.

In [35]:

hover = HoverTool(
    tooltips=[
        ("target_name", "@{target_name}"),
        ("t_planning", "@{t_planning}{0.000000}"),
    ]
)

Create the y-axis tick labels, one for each target name.

In [36]:

y_ticks = [(i, target) for target, i in target_to_y.items()]

Set all the other plot options and show the plot.

In [37]:

plot = plot.opts(
    opts.Scatter(
        width=600,
        height=400,
        tools=[hover, "pan", "wheel_zoom", "box_zoom", "reset", "save"],
        xlabel="Scheduled Observation Time (MJD)",
        ylabel="Target Name",
        title="Planned observations by target for Oct 29 2025",
        size=8,
        alpha=0.7,
        legend_position="right",
        legend_offset=(10, 0),
        yticks=y_ticks,
        show_legend=False,
    )
)
plot

Out[37]:

Figure 2: The time of the planned observation (x-axis) for each of the planned target fields (y-axis), for the night of Oct 29 2025.

6. Exercises for the learner¶

Repeat the analysis in Section 5, but for the next 24 hours of planned observations.

Warning: If "the next 24 hours" includes an inter-night gap (i.e., daytime), the inter-visit time gap distribution in Figure 1 will have two visits separated by 8 hours or more, and the x-axis values will extend to $\sim28000$ seconds or more. Figure 2 will also show a daytime gap with no planned visits.

In [ ]: