Visualizing Periodic Lightcurves

Visualizing Periodic Lightcurves#

In this tutorial, we will:

follow a guided walkthrough for visualizing lightcurves of a set of known periodic objects
crossmatch an object list with Rubin data using LSDB catalogs

Introduction#

This notebook demonstrates how to visualize lightcurves for periodic objects in the Rubin DP1 data.

[1]:

import astropy.units as u
import lsdb
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

from dask.distributed import Client
from io import StringIO
from upath import UPath

import warnings

# Disable plotting warnings
warnings.filterwarnings("ignore")

The Rubin HATS catalogs#

HATS (HEALPix Adaptive Tiling Scheme) is a HEALPix-based parquet format that enables fast parallelization of large scale workflows.
The data was imported and post-processed by the DASH pipeline.
It includes added-value columns for scientific use - e.g. PSF and science magnitudes, and timestamps for every source / forced source.

[2]:

hats_path = UPath("/rubin/lsdb_data")
diaobject_path = hats_path / "dia_object_collection"
object_path = hats_path / "object_collection"

Load the Rubin DP1 Catalog#

The relevant catalogs are diaObject and object. Each row corresponds to a single object with all its sources and nested sources embedded in separate columns.

[3]:

# Load DiaObject Collection
dia_catalog = lsdb.open_catalog(diaobject_path)
dia_catalog

[3]:

lsdb Catalog dia_object_lc:

	dec	diaObjectId	nDiaSources	ra	radecMjdTai	tract	diaObjectForcedSource	diaSource
npartitions=208
Order: 6, Pixel: 130	double[pyarrow]	int64[pyarrow]	int64[pyarrow]	double[pyarrow]	double[pyarrow]	int64[pyarrow]	nested<band: [string], coord_dec: [double], co...	nested<band: [string], centroid_flag: [bool], ...
Order: 6, Pixel: 136	...	...	...	...	...	...	...	...
...	...	...	...	...	...	...	...	...
Order: 11, Pixel: 36833621	...	...	...	...	...	...	...	...
Order: 7, Pixel: 143884	...	...	...	...	...	...	...	...

8 out of 140 available columns in the catalog have been loaded lazily, meaning no data has been read, only the catalog schema

[4]:

# Load Object Collection
obj_catalog = lsdb.open_catalog(object_path)
obj_catalog

[4]:

lsdb Catalog object_lc:

	coord_dec	coord_decErr	coord_ra	coord_raErr	g_psfFlux	g_psfFluxErr	g_psfMag	g_psfMagErr	i_psfFlux	i_psfFluxErr	i_psfMag	i_psfMagErr	objectId	patch	r_psfFlux	r_psfFluxErr	r_psfMag	r_psfMagErr	refBand	refFwhm	shape_flag	shape_xx	shape_xy	shape_yy	tract	u_psfFlux	u_psfFluxErr	u_psfMag	u_psfMagErr	x	xErr	y	y_psfFlux	y_psfFluxErr	y_psfMag	y_psfMagErr	yErr	z_psfFlux	z_psfFluxErr	z_psfMag	z_psfMagErr	objectForcedSource
npartitions=389
Order: 6, Pixel: 130	double[pyarrow]	float[pyarrow]	double[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	int64[pyarrow]	int64[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	string[pyarrow]	float[pyarrow]	bool[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	int64[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	double[pyarrow]	float[pyarrow]	double[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	nested<coord_ra: [double], coord_dec: [double]...
Order: 8, Pixel: 2176	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Order: 9, Pixel: 2302101	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Order: 7, Pixel: 143884	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...

42 out of 1304 available columns in the catalog have been loaded lazily, meaning no data has been read, only the catalog schema

Choose the variable objects#

We have a selection of 10 variable objects, which were found from previous analysis of forced photometry on science images:

[5]:

variables_csv = """ra,dec,period
94.95546,-24.73952,0.12095
95.30235,-25.27431,0.12248
94.91626,-24.69648,0.12038
95.12418,-25.04329,0.23554
58.83506,-48.79122,0.56335
94.92264,-25.23185,0.07672
94.72086,-25.05767,0.17559
94.97073,-25.13643,0.12048
59.12997,-48.78522,0.11628
94.72086,-25.05767,0.17554
"""
# We add the index as a column of the DataFrame so we can later retrieve
# all the Rubin data from difference and science imaging
variables_df = pd.read_csv(StringIO(variables_csv)).reset_index()
# Transform the DataFrame into a LSDB Catalog
variables_catalog = lsdb.from_dataframe(variables_df)
variables_catalog

[5]:

lsdb Catalog from_lsdb_dataframe:

	index	ra	dec	period
npartitions=2
Order: 2, Pixel: 80	int64[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]
Order: 5, Pixel: 8582	...	...	...	...

4 out of 4 available columns in the catalog have been loaded lazily, meaning no data has been read, only the catalog schema

Crossmatch the data#

Let’s crossmatch the Rubin data with our desired variable objects:

[6]:

XMATCH_RADIUS_ARCSEC = 0.2
variable_object = variables_catalog.crossmatch(
    obj_catalog, radius_arcsec=XMATCH_RADIUS_ARCSEC, suffixes=["_var", ""]
)
variable_dia = variables_catalog.crossmatch(
    dia_catalog, radius_arcsec=XMATCH_RADIUS_ARCSEC, suffixes=["_var", ""]
)
# The result will have all the columns in obj_catalog suffixed with `_obj`,
# all the columns in dia_catalog suffixed with `_dia`, as well as all the
# columns in `variables_df` suffixed with `_var_obj`
result = variable_object.join(
    variable_dia, left_on="index_var", right_on="index_var", suffixes=["_obj", "_dia"]
)
result

[6]:

lsdb Catalog from_lsdb_dataframe_x_object_lc:

	index_var_obj	ra_var_obj	dec_var_obj	period_var_obj	coord_dec_obj	coord_decErr_obj	coord_ra_obj	coord_raErr_obj	g_psfFlux_obj	g_psfFluxErr_obj	g_psfMag_obj	g_psfMagErr_obj	i_psfFlux_obj	i_psfFluxErr_obj	i_psfMag_obj	i_psfMagErr_obj	objectId_obj	patch_obj	r_psfFlux_obj	r_psfFluxErr_obj	r_psfMag_obj	r_psfMagErr_obj	refBand_obj	refFwhm_obj	shape_flag_obj	shape_xx_obj	shape_xy_obj	shape_yy_obj	tract_obj	u_psfFlux_obj	u_psfFluxErr_obj	u_psfMag_obj	u_psfMagErr_obj	x_obj	xErr_obj	y_obj	y_psfFlux_obj	y_psfFluxErr_obj	y_psfMag_obj	y_psfMagErr_obj	yErr_obj	z_psfFlux_obj	z_psfFluxErr_obj	z_psfMag_obj	z_psfMagErr_obj	objectForcedSource_obj	_dist_arcsec_obj	index_var_dia	ra_var_dia	dec_var_dia	period_var_dia	dec_dia	diaObjectId_dia	nDiaSources_dia	ra_dia	radecMjdTai_dia	tract_dia	diaObjectForcedSource_dia	diaSource_dia	_dist_arcsec_dia
npartitions=8
Order: 8, Pixel: 329721	int64[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	float[pyarrow]	double[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	int64[pyarrow]	int64[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	string[pyarrow]	float[pyarrow]	bool[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	int64[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	double[pyarrow]	float[pyarrow]	double[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	nested<coord_ra: [double], coord_dec: [double]...	double[pyarrow]	int64[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	int64[pyarrow]	int64[pyarrow]	double[pyarrow]	double[pyarrow]	int64[pyarrow]	nested<band: [string], coord_dec: [double], co...	nested<band: [string], centroid_flag: [bool], ...	double[pyarrow]
Order: 9, Pixel: 1324352	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Order: 9, Pixel: 2197038	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Order: 9, Pixel: 2197120	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...

60 out of 60 available columns in the catalog have been loaded lazily, meaning no data has been read, only the catalog schema

And apply filtering according to the quality flags:

[7]:

for column in ["objectForcedSource_obj", "diaSource_dia", "diaObjectForcedSource_dia"]:
    result = result.query(
        f"~{column}.psfFlux_flag"
        f" and ~{column}.pixelFlags_saturated"
        f" and ~{column}.pixelFlags_cr"
        f" and ~{column}.pixelFlags_bad"
    )
result

[7]:

lsdb Catalog from_lsdb_dataframe_x_object_lc:

	index_var_obj	ra_var_obj	dec_var_obj	period_var_obj	coord_dec_obj	coord_decErr_obj	coord_ra_obj	coord_raErr_obj	g_psfFlux_obj	g_psfFluxErr_obj	g_psfMag_obj	g_psfMagErr_obj	i_psfFlux_obj	i_psfFluxErr_obj	i_psfMag_obj	i_psfMagErr_obj	objectId_obj	patch_obj	r_psfFlux_obj	r_psfFluxErr_obj	r_psfMag_obj	r_psfMagErr_obj	refBand_obj	refFwhm_obj	shape_flag_obj	shape_xx_obj	shape_xy_obj	shape_yy_obj	tract_obj	u_psfFlux_obj	u_psfFluxErr_obj	u_psfMag_obj	u_psfMagErr_obj	x_obj	xErr_obj	y_obj	y_psfFlux_obj	y_psfFluxErr_obj	y_psfMag_obj	y_psfMagErr_obj	yErr_obj	z_psfFlux_obj	z_psfFluxErr_obj	z_psfMag_obj	z_psfMagErr_obj	objectForcedSource_obj	_dist_arcsec_obj	index_var_dia	ra_var_dia	dec_var_dia	period_var_dia	dec_dia	diaObjectId_dia	nDiaSources_dia	ra_dia	radecMjdTai_dia	tract_dia	diaObjectForcedSource_dia	diaSource_dia	_dist_arcsec_dia
npartitions=8
Order: 8, Pixel: 329721	int64[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	float[pyarrow]	double[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	int64[pyarrow]	int64[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	string[pyarrow]	float[pyarrow]	bool[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	int64[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	double[pyarrow]	float[pyarrow]	double[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	float[pyarrow]	nested<coord_ra: [double], coord_dec: [double]...	double[pyarrow]	int64[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	double[pyarrow]	int64[pyarrow]	int64[pyarrow]	double[pyarrow]	double[pyarrow]	int64[pyarrow]	nested<band: [string], coord_dec: [double], co...	nested<band: [string], centroid_flag: [bool], ...	double[pyarrow]
Order: 9, Pixel: 1324352	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Order: 9, Pixel: 2197038	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Order: 9, Pixel: 2197120	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...

60 out of 60 available columns in the catalog have been loaded lazily, meaning no data has been read, only the catalog schema

Notice that we instantiate a Dask Client to efficiently parallelize our computations:

[8]:

# Dask puts out more advisory logging than we care for in this tutorial.
# It takes some doing to quiet all of it, but this recipe works.

import dask

dask.config.set({"logging.distributed": "critical"})

import logging

# This also has to be done, for the above to be effective
logger = logging.getLogger("distributed")
logger.setLevel(logging.CRITICAL)

import warnings

# Finally, suppress the specific warning about Dask dashboard port usage
warnings.filterwarnings("ignore", message="Port 8787 is already in use.")

[9]:

with Client(n_workers=4, threads_per_worker=1) as client:
    # Sort light curves by variable object index for plotting purposes
    result_df = result.compute().sort_values("index_var_obj")
result_df

[9]:

index_var_obj

ra_var_obj

dec_var_obj

period_var_obj

coord_dec_obj

coord_decErr_obj

coord_ra_obj

coord_raErr_obj

g_psfFlux_obj

g_psfFluxErr_obj

g_psfMag_obj

g_psfMagErr_obj

i_psfFlux_obj

i_psfFluxErr_obj

i_psfMag_obj

i_psfMagErr_obj

objectId_obj

patch_obj

r_psfFlux_obj

r_psfFluxErr_obj

r_psfMag_obj

r_psfMagErr_obj

refBand_obj

refFwhm_obj

shape_flag_obj

shape_xx_obj

shape_xy_obj

shape_yy_obj

tract_obj

u_psfFlux_obj

u_psfFluxErr_obj

u_psfMag_obj

u_psfMagErr_obj

x_obj

xErr_obj

y_obj

y_psfFlux_obj

y_psfFluxErr_obj

y_psfMag_obj

y_psfMagErr_obj

yErr_obj

z_psfFlux_obj

z_psfFluxErr_obj

z_psfMag_obj

z_psfMagErr_obj

objectForcedSource_obj

_dist_arcsec_obj

index_var_dia

ra_var_dia

dec_var_dia

period_var_dia

dec_dia

diaObjectId_dia

nDiaSources_dia

ra_dia

radecMjdTai_dia

tract_dia

diaObjectForcedSource_dia

diaSource_dia

_dist_arcsec_dia

1456157018405184225

0

94.955460

-24.739520

0.120950

-24.739524

0.000000

94.955465

0.000000

96215.617188

40.481762

18.941887

0.000457

269817.406250

131.805710

17.822325

0.000530

614437746487865779

38

189773.562500

49.227470

18.204411

0.000282

i

0.765916

False

3.862049

-0.125740

3.714630

5526

18858.382812

89.659355

20.711239

0.005162

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0.001538

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323206.218750

600.387939

17.626301

0.002017

0.001516

192855.625000

97.654503

18.186920

0.000550

coord_ra	coord_dec	...	psfMagErr	midpointMjdTai
94.955465	-24.739524	...	0.006251	60634.323548
+179 rows	...	...	...	...

0.020477

0

94.955460

-24.739520

0.120950

-24.739521

614437746487853390

201

94.955459

60656.341913

5526

band	coord_dec	...	psfMagErr	visit
y	-24.739521	...	0.006255	2024111900358
+224 rows	...	...	...	...

band	centroid_flag	...	y	yErr
y	False	...	2521.369371	0.293006
+200 rows	...	...	...	...

0.006033

1450131236226156835

1

95.302350

-25.274310

0.122480

-25.274306

0.000000

95.302354

0.000000

38424.933594

34.274925

19.938467

0.000968

117489.132812

143.596176

18.725006

0.001327

614435616184087063

7

80723.289062

45.217892

19.132504

0.000608

i

0.775459

False

3.619916

-0.049815

3.491835

5526

7751.314941

183.145325

21.676561

0.025658

21076.081273

0.003834

1870.507517

153380.859375

948.672607

18.435572

0.006715

0.003639

109926.289062

130.206573

18.797247

0.001286

coord_ra	coord_dec	...	psfMagErr	midpointMjdTai
95.302354	-25.274306	...	0.010629	60634.324174
+102 rows	...	...	...	...

0.019504

1

95.302350

-25.274310

0.122480

-25.274309

614435616184074426

97

95.302351

60656.341097

5526

band	coord_dec	...	psfMagErr	visit
y	-25.274309	...	0.010632	2024111900359
+111 rows	...	...	...	...

band	centroid_flag	...	y	yErr
r	False	...	1725.2216	0.073185
+95 rows	...	...	...	...

0.006324

1456157750233453047

2

94.916260

-24.696480

0.120380

-24.696482

0.000000

94.916260

0.000000

333769.843750

75.333427

17.591383

0.000245

611969.625000

227.106125

16.933176

0.000403

614438502402106281

49

581614.875000

91.656525

16.988411

0.000171

i

0.718889

False

3.523738

-0.119928

3.450615

5526

97035.140625

136.521637

18.932678

0.001528

27418.764816

0.001104

12246.102667

874192.437500

862.398376

16.545982

0.001071

0.001096

666255.562500

155.905563

16.840899

0.000254

coord_ra	coord_dec	...	psfMagErr	midpointMjdTai
94.91626	-24.696482	...	0.002976	60634.323548
+166 rows	...	...	...	...

0.008658

2

94.916260

-24.696480

0.120380

-24.696486

614438502402097162

191

94.916259

60656.341913

5526

band	coord_dec	...	psfMagErr	visit
y	-24.696486	...	0.002976	2024111900358
+195 rows	...	...	...	...

band	centroid_flag	...	y	yErr
y	False	...	1700.155872	0.258548
+188 rows	...	...	...	...

0.023539

1456145241786549427

3

95.124180

-25.043290

0.235540

-25.043288

0.000000

95.124185

0.000000

1447216.250000

131.617188

15.998667

0.000099

1512019.250000

277.406952

15.951107

0.000199

614436990573616692

27

1507937.250000

147.580673

15.954041

0.000106

i

0.781935

False

4.078254

-0.103258

3.896416

5526

587695.562500

258.763855

16.977119

0.000478

23993.141039

0.000597

6019.453925

1376247.375000

874.364258

16.053259

0.000690

0.000565

1391431.750000

206.822601

16.041346

0.000161

coord_ra	coord_dec	...	psfMagErr	midpointMjdTai
95.124185	-25.043288	...	0.002251	60634.323548
+210 rows	...	...	...	...

0.017603

3

95.124180

-25.043290

0.235540

-25.043283

614436990573609020

190

95.124193

60656.341913

5526

band	coord_dec	...	psfMagErr	visit
y	-25.043283	...	0.002254	2024111900358
+248 rows	...	...	...	...

band	centroid_flag	...	y	yErr
y	False	...	2078.115065	0.65936
+179 rows	...	...	...	...

0.048900

2415759959093266587

4

58.835060

-48.791220

0.563350

-48.791216

0.000000

58.835057

0.000000

227908.046875

62.881378

18.005600

0.000300

298667.968750

97.474693

17.712029

0.000354

592913981740430064

26

284213.812500

63.170666

17.765886

0.000241

i

0.860226

False

4.747889

-0.143074

4.982483

2394

137513.906250

495.075928

18.554134

0.003909

20136.685019

0.001144

6990.269591

317141.250000

348.949615

17.646868

0.001195

0.001196

334122.062500

127.251488

17.590237

0.000414

coord_ra	coord_dec	...	psfMagErr	midpointMjdTai
58.835057	-48.791216	...	0.002282	60632.232337
+227 rows	...	...	...	...

0.015211

4

58.835060

-48.791220

0.563350

-48.791213

592913981740417050

207

58.835037

60655.279809

2394

band	coord_dec	...	psfMagErr	visit
r	-48.791213	...	0.002285	2024111700340
+234 rows	...	...	...	...

band	centroid_flag	...	y	yErr
r	False	...	1737.473935	0.420394
+199 rows	...	...	...	...

0.060615

1456140633585049253

5

94.922640

-25.231850

0.076720

-25.231852

0.000000

94.922636

0.000000

114717.398438

40.412148

18.750927

0.000382

124674.359375

96.265068

18.660557

0.000838

614435753623041404

9

117883.578125

40.532890

18.721367

0.000373

i

0.781278

False

3.951747

-0.079254

3.779037

5526

47365.300781

129.390182

19.711349

0.002966

27261.856164

0.002514

2608.901159

114787.703125

444.425446

18.750261

0.004204

0.002415

107830.750000

95.726501

18.818144

0.000964

coord_ra	coord_dec	...	psfMagErr	midpointMjdTai
94.922636	-25.231852	...	0.013571	60634.323548
+203 rows	...	...	...	...

0.014024

5

94.922640

-25.231850

0.076720

-25.231851

614435753623027782

186

94.922635

60656.341913

5526

band	coord_dec	...	psfMagErr	visit
y	-25.231851	...	0.01357	2024111900358
+212 rows	...	...	...	...

band	centroid_flag	...	y	yErr
i	False	...	2811.812419	0.146114
+184 rows	...	...	...	...

0.015657

1456149791804243835

6

94.720860

-25.057670

0.175590

-25.057669

0.000000

94.720857

0.000000

234904.062500

57.171268

17.972775

0.000264

340439.937500

150.168243

17.569899

0.000479

614429019114322311

11

307911.031250

67.220116

17.678936

0.000237

i

0.807315

False

4.236218

-0.149095

4.131883

5525

112731.046875

152.079132

18.769892

0.001465

4125.398950

0.001465

5752.096897

378513.843750

581.769897

17.454796

0.001669

0.001442

307040.531250

120.279778

17.682011

0.000425

coord_ra	coord_dec	...	psfMagErr	midpointMjdTai
94.720857	-25.057669	...	0.005256	60634.325417
+194 rows	...	...	...	...

0.010659

6

94.720860

-25.057670

0.175590

-25.057664

614429019114307699

180

94.720857

60656.341097

5525

band	coord_dec	...	psfMagErr	visit
y	-25.057664	...	0.005278	2024111900360
+206 rows	...	...	...	...

band	centroid_flag	...	y	yErr
y	False	...	2134.129651	0.382894
+177 rows	...	...	...	...

0.024753

2415669000192289034

8

59.129970

-48.785220

0.116280

-48.785224

0.000000

59.129967

0.000000

15816.786133

24.092821

20.902205

0.001654

41957.535156

51.133236

19.842976

0.001323

592913913020955088

25

30814.417969

26.435709

20.178116

0.000931

i

0.855076

False

4.690122

-0.119492

4.728663

2394

3558.745361

227.412216

22.521757

0.069476

16639.578774

0.004362

7111.261595

52567.726562

284.252258

19.598202

0.005871

0.004407

44020.945312

86.908951

19.790852

0.002144

coord_ra	coord_dec	...	psfMagErr	midpointMjdTai
59.129967	-48.785224	...	0.011159	60632.232337
+220 rows	...	...	...	...

0.015603

8

59.129970

-48.785220

0.116280

-48.785226

592913913020940296

152

59.129967

60655.279414

2394

band	coord_dec	...	psfMagErr	visit
r	-48.785226	...	0.01116	2024111700340
+232 rows	...	...	...	...

band	centroid_flag	...	y	yErr
r	False	...	821.532201	0.375699
+147 rows	...	...	...	...

0.023348

1456149791804243835

9

94.720860

-25.057670

0.175540

-25.057669

0.000000

94.720857

0.000000

234904.062500

57.171268

17.972775

0.000264

340439.937500

150.168243

17.569899

0.000479

614429019114322311

11

307911.031250

67.220116

17.678936

0.000237

i

0.807315

False

4.236218

-0.149095

4.131883

5525

112731.046875

152.079132

18.769892

0.001465

4125.398950

0.001465

5752.096897

378513.843750

581.769897

17.454796

0.001669

0.001442

307040.531250

120.279778

17.682011

0.000425

coord_ra	coord_dec	...	psfMagErr	midpointMjdTai
94.720857	-25.057669	...	0.005256	60634.325417
+194 rows	...	...	...	...

0.010659

9

94.720860

-25.057670

0.175540

-25.057664

614429019114307699

180

94.720857

60656.341097

5525

band	coord_dec	...	psfMagErr	visit
y	-25.057664	...	0.005278	2024111900360
+206 rows	...	...	...	...

band	centroid_flag	...	y	yErr
y	False	...	2134.129651	0.382894
+177 rows	...	...	...	...

0.024753

9 rows x 60 columns

Plot the lightcurves from the Rubin Data#

The following utility methods allow us to plot light curves from DIA source, DIA forced source and forcedSource, for each of our matches.

[10]:

COLORS = {
    "u": "#56b4e9",
    "g": "#009e73",
    "r": "#f0e442",
    "i": "#cc79a7",
    "z": "#d55e00",
    "y": "#0072b2",
}

In the magnitude scale#

[11]:

def plot_mag_lightcurves(ax, row):
    """Plot magnitude light curves from DIA source, DIA forced source and forcedSource"""
    # Define flux types for each dataset
    datasets = [
        ("scienceFlux", "diaSourceTable_tract", row.diaSource_dia),
        ("psfDiffFlux", "forcedSourceOnDiaObjectTable", row.diaObjectForcedSource_dia),
        ("psfFlux", "forcedSourceTable", row.objectForcedSource_obj),
    ]
    all_mags = [[], []]  # To store magnitudes for each row
    for i, (flux_col, table_name, lc) in enumerate(datasets):
        title = f"{flux_col} from {table_name}"
        flux_err_col = f"{flux_col}Err"
        ax[0, i].set_title(title)
        # Compute phase
        lc = lc.assign(
            phase=(lc.midpointMjdTai - lc.midpointMjdTai.loc[lc.psfFlux.idxmax()])
            % row.period_var_obj
            / row.period_var_obj
        )
        # First row: original light curve
        all_mags[0].extend(
            plot_mag_scale(
                ax[0, i],
                lc,
                flux_col,
                flux_err_col,
                x_name="midpointMjdTai",
                x_label="MJD",
                show_legend=(i == 0),
            )
        )
        # Second row: folded light curve
        all_mags[1].extend(
            plot_mag_scale(ax[1, i], lc, flux_col, flux_err_col, x_name="phase", x_label="Phase")
        )
    return all_mags


def plot_mag_scale(ax, lc, flux_col, flux_err_col, x_name, x_label, show_legend=False):
    """Plot light curves in magnitude scale"""
    mag_values = []  # Store magnitudes for setting axis limits
    for band, color in COLORS.items():
        band_lc = lc.query(f"band == '{band}'")
        # Compute magnitudes and errors
        mag, magErr = create_mag_errors(band_lc[flux_col], band_lc[flux_err_col])
        ax.errorbar(
            band_lc[x_name],
            mag,
            magErr,
            fmt="o",
            label=band,
            color=color,
            alpha=1,
            markersize=5,
            capsize=3,
            elinewidth=1,
        )
        mag_values.extend(mag.dropna().values)  # Collect magnitude values
    ax.set_xlabel(x_label)
    ax.set_ylabel("Magnitude (AB)")
    ax.invert_yaxis()  # Magnitudes are plotted with brighter objects lower
    if show_legend:
        ax.legend(loc="lower right", fontsize=12)  # Show legend in top-left panel only
    return mag_values  # Return magnitudes for axis scaling


def create_mag_errors(sciFlux, sciFluxErr):
    """Move flux into magnitudes and calculate the error on the magnitude"""
    mag = u.nJy.to(u.ABmag, sciFlux)
    upper_mag = u.nJy.to(u.ABmag, sciFlux + sciFluxErr)
    lower_mag = u.nJy.to(u.ABmag, sciFlux - sciFluxErr)
    magErr = -(upper_mag - lower_mag) / 2
    return mag, magErr


def scale_mag_y_axis(ax, all_mags):
    """Set uniform y-axis scaling for each plot row"""
    for row_idx in range(2):
        if all_mags[row_idx]:  # Ensure we have data
            ymin, ymax = np.nanmin(all_mags[row_idx]), np.nanmax(all_mags[row_idx])
            for i in range(3):  # Apply limits to all columns in the row
                ax[row_idx, i].set_ylim(ymax + 0.1, ymin - 0.1)  # Keep magnitude inverted

[12]:

for _, row in result_df.iterrows():
    fig, ax = plt.subplots(2, 3, figsize=(16, 8), sharex="row")  # 2 rows, 3 columns
    fig.suptitle(
        f"RA={row.ra_var_obj:.5f}, Dec={row.dec_var_obj:.5f}",
        fontsize=16,
    )
    all_mags = plot_mag_lightcurves(ax, row)
    scale_mag_y_axis(ax, all_mags)
    plt.tight_layout()
    plt.show()

../../_images/tutorials_pre_executed_visualize_periodic_lcs_20_0.png

../../_images/tutorials_pre_executed_visualize_periodic_lcs_20_1.png

../../_images/tutorials_pre_executed_visualize_periodic_lcs_20_2.png

../../_images/tutorials_pre_executed_visualize_periodic_lcs_20_3.png

../../_images/tutorials_pre_executed_visualize_periodic_lcs_20_4.png

../../_images/tutorials_pre_executed_visualize_periodic_lcs_20_5.png

../../_images/tutorials_pre_executed_visualize_periodic_lcs_20_6.png

../../_images/tutorials_pre_executed_visualize_periodic_lcs_20_7.png

../../_images/tutorials_pre_executed_visualize_periodic_lcs_20_8.png

In the flux scale#

[13]:

def plot_flux_lightcurves(ax, row):
    """Plot flux light curves from DIA source, DIA forced source and forcedSource"""
    # Define flux types for each dataset
    datasets = [
        ("scienceFlux", "diaSourceTable_tract", row.diaSource_dia),
        ("psfDiffFlux", "forcedSourceOnDiaObjectTable", row.diaObjectForcedSource_dia),
        ("psfFlux", "forcedSourceTable", row.objectForcedSource_obj),
    ]
    all_flux = [[], [], []]  # To store flux for each row
    for i, (flux_col, table_name, lc) in enumerate(datasets):
        title = f"{flux_col} from {table_name}"
        flux_err_col = f"{flux_col}Err"
        ax[0, i].set_title(title)
        # Compute phase
        lc = lc.assign(
            phase=(lc.midpointMjdTai - lc.midpointMjdTai.loc[lc.psfFlux.idxmax()])
            % row.period_var_obj
            / row.period_var_obj
        )
        # First row: original light curve
        all_flux[i].extend(
            plot_flux_scale(
                ax[0, i],
                lc,
                flux_col,
                flux_err_col,
                x_name="midpointMjdTai",
                x_label="MJD",
                show_legend=(i == 0),
            )
        )
        # Second row: folded light curve
        all_flux[i].extend(
            plot_flux_scale(ax[1, i], lc, flux_col, flux_err_col, x_name="phase", x_label="Phase")
        )
    return all_flux


def plot_flux_scale(ax, lc, flux_col, flux_err_col, x_name, x_label, show_legend=False):
    """Function to plot light curves in flux scale"""
    flux_values = []  # Store flux values for setting axis limits
    for band, color in COLORS.items():
        band_lc = lc.query(f"band == '{band}'")
        # Extract flux values and errors directly
        flux = band_lc[flux_col]
        flux_err = band_lc[flux_err_col]
        ax.errorbar(
            band_lc[x_name],
            flux,
            flux_err,
            fmt="o",
            label=band,
            color=color,
            alpha=1,
            markersize=5,
            capsize=3,
            elinewidth=1,
        )
        flux_values.extend(flux.dropna().values)  # Collect flux values
    ax.set_xlabel(x_label, fontsize=14)  # Increased font size
    ax.set_ylabel(f"{flux_col} (nJy)", fontsize=14)  # Increased font size
    if show_legend:
        ax.legend(loc="lower right", fontsize=12)  # Show legend in top-left panel only
    return flux_values  # Return flux values for axis scaling


def scale_flux_y_axis(ax, all_flux):
    """Set uniform y-axis scaling for first and third columns, while middle remains independent and symmetric"""
    for row_idx in range(2):
        for col_idx in [0, 2]:  # Apply shared limits to first and third columns
            if all_flux[col_idx]:  # Ensure we have data
                ymin, ymax = np.nanmin(all_flux[col_idx]), np.nanmax(all_flux[col_idx])
                ax[row_idx, col_idx].set_ylim(ymin - 0.1 * abs(ymin), ymax + 0.1 * abs(ymax))  # Add buffer
        # Middle column (psfDiffFlux) gets independent, symmetric scaling
        if all_flux[1]:
            max_abs_flux = np.nanmax(np.abs(all_flux[1]))  # Find the maximum absolute value
            ax[row_idx, 1].set_ylim(-max_abs_flux * 1.1, max_abs_flux * 1.1)  # Symmetric range around 0

[14]:

for _, row in result_df.iterrows():
    fig, ax = plt.subplots(2, 3, figsize=(16, 8), sharex="row")  # 2 rows, 3 columns
    fig.suptitle(
        f"RA={row.ra_var_obj:.5f}, Dec={row.dec_var_obj:.5f}",
        fontsize=16,
    )
    all_flux = plot_flux_lightcurves(ax, row)
    scale_flux_y_axis(ax, all_flux)
    plt.tight_layout()
    plt.show()