Basic Usage

Setup

This tutorial notebook will walk you through the basic usage of the OTTER API. First make sure you followed the installation structions to install otter and the data. First the imports we will need for the notebook

import os
import otter

from astropy.coordinates import SkyCoord
from astropy import units as u

import matplotlib.pyplot as plt
import numpy as np

We can initiate the otter database object and then get metadata of all of the data. From there, we can easily get the default redshift for each object and histogram them.

Note: When calling get_meta it does not return the photometry. This is to make metadata queries much quicker. See below for also accessing the photometry along with metadata.

db = otter.Otter()
meta = db.get_meta(save=False)
redshifts = np.array([t.get_redshift() for t in meta if "distance" in t and len(t["distance"]) > 0])
fig, ax = plt.subplots()
ax.hist(redshifts.astype(float), bins=20)
ax.set_ylabel("N")
ax.set_xlabel("z")

Attempting to login to https://otter.idies.jhu.edu/api with the following credentials:
username: user-guest
password: test

Text(0.5, 0, 'z')

Querying the OTTER Catalog

Now that we have the catalog read in an initiated, we can query it for the data we want. Say we want to get all of the data associated with the TDE Sw J1644+57, we can use

swj1644_list = db.query(names='Sw J1644+57')
swj1644_list

[Transient(
        Name: Sw J1644+57,
        Keys: dict_keys(['_key', '_id', '_rev', 'classification', 'name', 'coordinate', 'filter_alias', 'photometry', 'reference_alias', 'distance', 'host', 'date_reference', 'schema_version'])
 )]

Which you can see is a list of otter Transient objects. You can essentially treat this like a python dictionary with some benefits:

• It has additional helpful methods to access and clean the data

• You can use the HDF5 slash-syntax. Ex.) swj1644['name/alias'] will give you all of the aliases from the alias subkey in the name key

swj1644 = swj1644_list[0] # just to get rid of the list

swj1644['name/alias']

[{'value': 'GRB 110328A', 'reference': ['2011Sci...333..203B']},
 {'value': 'Swift J1644+57',
  'reference': ['2017ApJ...838..149A',
   '2011Sci...333..203B',
   '2023PASP..135c4101G']},
 {'value': 'Sw J1644+57', 'reference': ['Swift', '2011Natur.476..425Z']},
 {'value': 'Swift J164449.3+573451', 'reference': ['2011Sci...333..203B']},
 {'value': 'SwJ1644+57',
  'reference': ['2011Natur.476..425Z', '2011Natur.476..425Z']}]

So this TDE has multiple aliases associated with it! Let’s try to query by a different one

db.query(names='GRB 110328A')[0]

Transient(
        Name: Sw J1644+57,
        Keys: dict_keys(['_key', '_id', '_rev', 'classification', 'name', 'coordinate', 'filter_alias', 'photometry', 'reference_alias', 'distance', 'host', 'date_reference', 'schema_version'])
)

And look at that, it gives us the same answer! We can also query by a list of names which will give us a list of transient objects back:

db.query(names=['Sw J1644+57', '2018hyz', '2018zr'])

[Transient(
        Name: Sw J1644+57,
        Keys: dict_keys(['_key', '_id', '_rev', 'classification', 'name', 'coordinate', 'filter_alias', 'photometry', 'reference_alias', 'distance', 'host', 'date_reference', 'schema_version'])
 ),
 Transient(
        Name: 2018hyz,
        Keys: dict_keys(['_key', '_id', '_rev', 'reference_alias', 'classification', 'coordinate', 'distance', 'name', 'date_reference', 'schema_version', 'filter_alias', 'photometry'])
 ),
 Transient(
        Name: 2018zr,
        Keys: dict_keys(['_key', '_id', '_rev', 'reference_alias', 'classification', 'coordinate', 'distance', 'name', 'date_reference', 'host', 'schema_version', 'filter_alias', 'photometry'])
 )]

We can also search OTTER for anything within a certain redshift range, let’s look for everything between \(0.1 < z <0.2\):

db.query(minz=0.1, maxz=0.2)

[Transient(
        Name: 2021ack,
        Keys: dict_keys(['_key', '_id', '_rev', 'reference_alias', 'classification', 'coordinate', 'distance', 'name', 'date_reference', 'filter_alias', 'photometry'])
 ),
 Transient(
        Name: iPTF16axa,
        Keys: dict_keys(['_key', '_id', '_rev', 'classification', 'name', 'reference_alias', 'photometry', 'coordinate', 'filter_alias', 'date_reference', 'distance', 'host', 'schema_version'])
 ),
 Transient(
        Name: J141036,
        Keys: dict_keys(['_key', '_id', '_rev', 'classification', 'coordinate', 'distance', 'date_reference', 'schema_version', 'name', 'reference_alias', 'filter_alias', 'photometry', 'host'])
 ),
 Transient(
        Name: 2021utq,
        Keys: dict_keys(['_key', '_id', '_rev', 'reference_alias', 'classification', 'coordinate', 'distance', 'name', 'date_reference', 'filter_alias', 'photometry'])
 ),
 Transient(
        Name: Dougie,
        Keys: dict_keys(['_key', '_id', '_rev', 'classification', 'coordinate', 'distance', 'date_reference', 'schema_version', 'name', 'reference_alias', 'filter_alias', 'photometry', 'host'])
 ),
 Transient(
        Name: 2021lo,
        Keys: dict_keys(['_key', '_id', '_rev', 'reference_alias', 'classification', 'coordinate', 'distance', 'name', 'date_reference', 'filter_alias', 'photometry'])
 ),
 Transient(
        Name: J030257,
        Keys: dict_keys(['_key', '_id', '_rev', 'classification', 'coordinate', 'distance', 'date_reference', 'schema_version', 'name', 'reference_alias', 'filter_alias', 'photometry', 'host'])
 ),
 Transient(
        Name: 2024qab,
        Keys: dict_keys(['_key', '_id', '_rev', 'reference_alias', 'classification', 'coordinate', 'distance', 'name', 'date_reference', 'filter_alias', 'photometry'])
 ),
 Transient(
        Name: J113527,
        Keys: dict_keys(['_key', '_id', '_rev', 'classification', 'coordinate', 'distance', 'date_reference', 'schema_version', 'name', 'reference_alias', 'filter_alias', 'photometry', 'host'])
 ),
 Transient(
        Name: 2021qxv,
        Keys: dict_keys(['_key', '_id', '_rev', 'reference_alias', 'classification', 'coordinate', 'distance', 'name', 'date_reference', 'host', 'filter_alias', 'photometry'])
 ),
 Transient(
        Name: 2021jjm,
        Keys: dict_keys(['_key', '_id', '_rev', 'reference_alias', 'classification', 'coordinate', 'distance', 'name', 'date_reference', 'filter_alias', 'photometry'])
 ),
 Transient(
        Name: 2022czy,
        Keys: dict_keys(['_key', '_id', '_rev', 'coordinate', 'classification', 'name', 'reference_alias', 'photometry', 'filter_alias', 'distance', 'date_reference'])
 ),
 Transient(
        Name: SRGE J135514.8+311605,
        Keys: dict_keys(['_key', '_id', '_rev', 'schema_version', 'name', 'classification', 'distance', 'coordinate', 'date_reference', 'host', 'reference_alias'])
 ),
 Transient(
        Name: SRGE J071310.6+725627,
        Keys: dict_keys(['_key', '_id', '_rev', 'schema_version', 'name', 'classification', 'distance', 'coordinate', 'date_reference', 'host', 'reference_alias'])
 ),
 Transient(
        Name: J145851,
        Keys: dict_keys(['_key', '_id', '_rev', 'classification', 'coordinate', 'distance', 'date_reference', 'schema_version', 'name', 'reference_alias', 'filter_alias', 'photometry', 'host'])
 ),
 Transient(
        Name: 2021crk,
        Keys: dict_keys(['_key', '_id', '_rev', 'reference_alias', 'classification', 'coordinate', 'distance', 'name', 'date_reference', 'filter_alias', 'photometry'])
 ),
 Transient(
        Name: 2021axu,
        Keys: dict_keys(['_key', '_id', '_rev', 'reference_alias', 'classification', 'coordinate', 'distance', 'name', 'date_reference', 'filter_alias', 'photometry'])
 ),
 Transient(
        Name: eRASSt J234403-352640,
        Keys: dict_keys(['_key', '_id', '_rev', 'name', 'coordinate', 'distance', 'classification', 'date_reference', 'photometry', 'filter_alias', 'reference_alias'])
 ),
 Transient(
        Name: 2019meg,
        Keys: dict_keys(['_key', '_id', '_rev', 'coordinate', 'name', 'reference_alias', 'date_reference', 'classification', 'filter_alias', 'photometry', 'distance'])
 ),
 Transient(
        Name: DES14C1kia,
        Keys: dict_keys(['_key', '_id', '_rev', 'classification', 'name', 'reference_alias', 'photometry', 'coordinate', 'filter_alias', 'date_reference', 'distance', 'host', 'schema_version'])
 ),
 Transient(
        Name: SRGE J091747.6+524821,
        Keys: dict_keys(['_key', '_id', '_rev', 'schema_version', 'name', 'classification', 'distance', 'coordinate', 'date_reference', 'host', 'reference_alias'])
 ),
 Transient(
        Name: SRGE J133053.3+734824,
        Keys: dict_keys(['_key', '_id', '_rev', 'schema_version', 'name', 'classification', 'distance', 'coordinate', 'date_reference', 'host', 'reference_alias'])
 ),
 Transient(
        Name: PTF10nuj,
        Keys: dict_keys(['_key', '_id', '_rev', 'classification', 'coordinate', 'distance', 'date_reference', 'schema_version', 'name', 'reference_alias', 'filter_alias', 'photometry', 'host'])
 ),
 Transient(
        Name: J155223,
        Keys: dict_keys(['_key', '_id', '_rev', 'classification', 'coordinate', 'distance', 'date_reference', 'schema_version', 'name', 'reference_alias', 'filter_alias', 'photometry', 'host'])
 ),
 Transient(
        Name: 2020ddv,
        Keys: dict_keys(['_key', '_id', '_rev', 'coordinate', 'name', 'reference_alias', 'date_reference', 'classification', 'filter_alias', 'photometry', 'distance'])
 ),
 Transient(
        Name: SRGE J161001.2+330121,
        Keys: dict_keys(['_key', '_id', '_rev', 'schema_version', 'name', 'classification', 'distance', 'coordinate', 'date_reference', 'host', 'reference_alias'])
 ),
 Transient(
        Name: PTF09djl,
        Keys: dict_keys(['_key', '_id', '_rev', 'classification', 'coordinate', 'distance', 'date_reference', 'schema_version', 'name', 'reference_alias', 'filter_alias', 'photometry', 'host'])
 ),
 Transient(
        Name: 3XMM J150052.0+015452,
        Keys: dict_keys(['_key', '_id', '_rev', 'classification', 'coordinate', 'distance', 'schema_version', 'name', 'reference_alias', 'date_reference', 'host'])
 ),
 Transient(
        Name: J133837,
        Keys: dict_keys(['_key', '_id', '_rev', 'classification', 'coordinate', 'distance', 'date_reference', 'schema_version', 'name', 'reference_alias', 'filter_alias', 'photometry', 'host'])
 ),
 Transient(
        Name: PS1-10jh,
        Keys: dict_keys(['_key', '_id', '_rev', 'classification', 'name', 'reference_alias', 'photometry', 'distance', 'coordinate', 'filter_alias', 'date_reference', 'host', 'schema_version'])
 ),
 Transient(
        Name: RX J1420+53,
        Keys: dict_keys(['_key', '_id', '_rev', 'classification', 'coordinate', 'distance', 'date_reference', 'schema_version', 'name', 'reference_alias', 'filter_alias', 'photometry', 'host'])
 ),
 Transient(
        Name: 2017bcc,
        Keys: dict_keys(['_key', '_id', '_rev', 'reference_alias', 'classification', 'coordinate', 'distance', 'name', 'date_reference', 'host', 'schema_version', 'filter_alias', 'photometry'])
 ),
 Transient(
        Name: 2020abri,
        Keys: dict_keys(['_key', '_id', '_rev', 'reference_alias', 'coordinate', 'distance', 'name', 'date_reference', 'classification', 'filter_alias', 'photometry'])
 ),
 Transient(
        Name: 2022hvp,
        Keys: dict_keys(['_key', '_id', '_rev', 'reference_alias', 'classification', 'coordinate', 'distance', 'name', 'date_reference', 'filter_alias', 'photometry'])
 ),
 Transient(
        Name: 2019bhf,
        Keys: dict_keys(['_key', '_id', '_rev', 'reference_alias', 'classification', 'coordinate', 'distance', 'name', 'date_reference', 'filter_alias', 'photometry'])
 ),
 Transient(
        Name: 2022agi,
        Keys: dict_keys(['_key', '_id', '_rev', 'coordinate', 'name', 'reference_alias', 'date_reference', 'classification', 'host', 'schema_version', 'filter_alias', 'photometry', 'distance'])
 ),
 Transient(
        Name: CSS100217,
        Keys: dict_keys(['_key', '_id', '_rev', 'classification', 'coordinate', 'distance', 'date_reference', 'schema_version', 'name', 'reference_alias', 'filter_alias', 'photometry', 'host'])
 ),
 Transient(
        Name: J094608,
        Keys: dict_keys(['_key', '_id', '_rev', 'classification', 'coordinate', 'distance', 'schema_version', 'name', 'reference_alias', 'host'])
 ),
 Transient(
        Name: 2019lwu,
        Keys: dict_keys(['_key', '_id', '_rev', 'reference_alias', 'classification', 'coordinate', 'distance', 'name', 'date_reference', 'filter_alias', 'photometry'])
 ),
 Transient(
        Name: 2019mha,
        Keys: dict_keys(['_key', '_id', '_rev', 'coordinate', 'name', 'reference_alias', 'date_reference', 'classification', 'filter_alias', 'photometry', 'distance'])
 ),
 Transient(
        Name: 2020opy,
        Keys: dict_keys(['_key', '_id', '_rev', 'coordinate', 'name', 'reference_alias', 'date_reference', 'classification', 'host', 'schema_version', 'filter_alias', 'photometry', 'distance'])
 ),
 Transient(
        Name: SDSSJ1311,
        Keys: dict_keys(['_key', '_id', '_rev', 'classification', 'name', 'reference_alias', 'photometry', 'distance', 'coordinate', 'filter_alias', 'date_reference', 'host', 'schema_version'])
 ),
 Transient(
        Name: SRGE J013204.6+122236,
        Keys: dict_keys(['_key', '_id', '_rev', 'schema_version', 'name', 'classification', 'distance', 'coordinate', 'date_reference', 'host', 'reference_alias'])
 ),
 Transient(
        Name: TDE1,
        Keys: dict_keys(['_key', '_id', '_rev', 'classification', 'name', 'reference_alias', 'photometry', 'distance', 'coordinate', 'filter_alias', 'date_reference', 'host', 'schema_version'])
 ),
 Transient(
        Name: D23H-1,
        Keys: dict_keys(['_key', '_id', '_rev', 'classification', 'name', 'reference_alias', 'photometry', 'distance', 'coordinate', 'filter_alias', 'date_reference', 'host', 'schema_version'])
 ),
 Transient(
        Name: J091225,
        Keys: dict_keys(['_key', '_id', '_rev', 'classification', 'coordinate', 'distance', 'date_reference', 'schema_version', 'name', 'reference_alias', 'filter_alias', 'photometry', 'host'])
 ),
 Transient(
        Name: 2017eqx,
        Keys: dict_keys(['_key', '_id', '_rev', 'coordinate', 'name', 'reference_alias', 'date_reference', 'classification', 'host', 'schema_version', 'filter_alias', 'photometry', 'distance'])
 ),
 Transient(
        Name: 2021uvz,
        Keys: dict_keys(['_key', '_id', '_rev', 'reference_alias', 'classification', 'coordinate', 'distance', 'name', 'date_reference', 'filter_alias', 'photometry'])
 ),
 Transient(
        Name: 2018lni,
        Keys: dict_keys(['_key', '_id', '_rev', 'coordinate', 'name', 'reference_alias', 'date_reference', 'classification', 'filter_alias', 'photometry', 'distance'])
 ),
 Transient(
        Name: J233454,
        Keys: dict_keys(['_key', '_id', '_rev', 'classification', 'coordinate', 'distance', 'schema_version', 'name', 'reference_alias', 'host'])
 ),
 Transient(
        Name: 2022rz,
        Keys: dict_keys(['_key', '_id', '_rev', 'reference_alias', 'classification', 'coordinate', 'distance', 'name', 'date_reference', 'filter_alias', 'photometry'])
 ),
 Transient(
        Name: PTF09axc,
        Keys: dict_keys(['_key', '_id', '_rev', 'classification', 'name', 'reference_alias', 'photometry', 'distance', 'coordinate', 'filter_alias', 'date_reference', 'host', 'schema_version'])
 ),
 Transient(
        Name: 2021uqv,
        Keys: dict_keys(['_key', '_id', '_rev', 'reference_alias', 'classification', 'coordinate', 'distance', 'name', 'date_reference', 'filter_alias', 'photometry'])
 ),
 Transient(
        Name: 2019cho,
        Keys: dict_keys(['_key', '_id', '_rev', 'coordinate', 'name', 'reference_alias', 'date_reference', 'classification', 'filter_alias', 'photometry', 'distance'])
 ),
 Transient(
        Name: 2021jsg,
        Keys: dict_keys(['_key', '_id', '_rev', 'reference_alias', 'classification', 'coordinate', 'distance', 'name', 'date_reference', 'filter_alias', 'photometry'])
 ),
 Transient(
        Name: 3XMM J152130.7+074916,
        Keys: dict_keys(['_key', '_id', '_rev', 'classification', 'coordinate', 'distance', 'date_reference', 'schema_version', 'name', 'reference_alias', 'filter_alias', 'photometry', 'host'])
 ),
 Transient(
        Name: SRGE J144738.4+671821,
        Keys: dict_keys(['_key', '_id', '_rev', 'schema_version', 'name', 'classification', 'distance', 'coordinate', 'date_reference', 'host', 'reference_alias'])
 ),
 Transient(
        Name: OGLE17aaj,
        Keys: dict_keys(['_key', '_id', '_rev', 'classification', 'coordinate', 'distance', 'date_reference', 'schema_version', 'name', 'reference_alias', 'host'])
 ),
 Transient(
        Name: OGLE16aaa,
        Keys: dict_keys(['_key', '_id', '_rev', 'classification', 'coordinate', 'distance', 'date_reference', 'schema_version', 'name', 'reference_alias', 'filter_alias', 'photometry', 'host'])
 ),
 Transient(
        Name: 2XMM J123103.2+110648,
        Keys: dict_keys(['_key', '_id', '_rev', 'classification', 'coordinate', 'distance', 'schema_version', 'name', 'reference_alias', 'date_reference', 'host'])
 ),
 Transient(
        Name: SDSSJ1201,
        Keys: dict_keys(['_key', '_id', '_rev', 'classification', 'name', 'reference_alias', 'photometry', 'distance', 'coordinate', 'filter_alias', 'date_reference', 'host', 'schema_version'])
 ),
 Transient(
        Name: 2022csn,
        Keys: dict_keys(['_key', '_id', '_rev', 'name', 'coordinate', 'distance', 'classification', 'date_reference', 'schema_version', 'reference_alias'])
 ),
 Transient(
        Name: 2023ugy,
        Keys: dict_keys(['_key', '_id', '_rev', 'name', 'coordinate', 'distance', 'classification', 'date_reference', 'schema_version', 'reference_alias'])
 ),
 Transient(
        Name: 2023kvy,
        Keys: dict_keys(['_key', '_id', '_rev', 'name', 'coordinate', 'distance', 'classification', 'date_reference', 'schema_version', 'reference_alias'])
 )]

WOW! That’s a lot of TDEs. But that’s expected since many of them are discovered nearby. What if we look for everything with a redshift over 1?

db.query(minz=1)

[Transient(
        Name: Swift J2058.4+0516,
        Keys: dict_keys(['_key', '_id', '_rev', 'classification', 'coordinate', 'date_reference', 'distance', 'filter_alias', 'host', 'name', 'photometry', 'reference_alias', 'schema_version'])
 ),
 Transient(
        Name: 2022cmc,
        Keys: dict_keys(['_key', '_id', '_rev', 'coordinate', 'name', 'reference_alias', 'date_reference', 'classification', 'schema_version', 'filter_alias', 'photometry', 'distance'])
 ),
 Transient(
        Name: CXOU J0332,
        Keys: dict_keys(['_key', '_id', '_rev', 'classification', 'coordinate', 'date_reference', 'schema_version', 'name', 'reference_alias', 'distance', 'host'])
 )]

Ah makes sense, it is just those super bright jetted TDEs!

Cone Searches

Moving on from general querys, we can perform cone searches on the catalog using the otter.Otter.cone_search method. Let’s do this for objects within 15 degrees of the ecliptic pole.

# first construct a SkyCoord
coord = SkyCoord('18h00m00.0s', '+66d33m38.84s', unit=(u.hourangle, u.deg))
radius = (15*u.deg).to(u.arcsec).value # must just provide it as a float

db.cone_search(coord, radius=radius)

[Transient(
        Name: RX J1624+75,
        Keys: dict_keys(['_key', '_id', '_rev', 'classification', 'name', 'reference_alias', 'photometry', 'distance', 'coordinate', 'filter_alias', 'date_reference', 'host', 'schema_version'])
 ),
 Transient(
        Name: WTP14adeqka,
        Keys: dict_keys(['_key', '_id', '_rev', 'name', 'coordinate', 'distance', 'date_reference', 'classification', 'photometry', 'filter_alias', 'reference_alias'])
 ),
 Transient(
        Name: VT J1752,
        Keys: dict_keys(['_key', '_id', '_rev', 'name', 'coordinate', 'distance', 'classification', 'schema_version', 'reference_alias'])
 ),
 Transient(
        Name: Sw J1644+57,
        Keys: dict_keys(['_key', '_id', '_rev', 'classification', 'name', 'coordinate', 'filter_alias', 'photometry', 'reference_alias', 'distance', 'host', 'date_reference', 'schema_version'])
 )]

Sample Selection

Given that OTTER includes many transients that are only classified based on photometric information, we provide numerous flags for selecting a subsamble of events based on your scientific goals:

1. spec_classed is True if the event is spectroscopically classified

2. unambiguous is True if the community unambiguously agrees on this classification

3. class_confidence_threshold let’s you set a minimum for the “confidence” flag C

As an example, to select all of the events that are spectroscopically classified as TDEs you would do

spec_classed_tdes = db.query(classification="TDE", spec_classed=True)

len(spec_classed_tdes)

144

Or, to get all events that unambiguously spectrocsopically classified as TDEs:

gold_spec_classed = db.query(classification="TDE", spec_classed=True, unambiguous=True)
len(gold_spec_classed)

141

We can then see the TDE candidates that have classification spectra but their true nature is still up for debate:

set(t.default_name for t in spec_classed_tdes) - set(t.default_name for t in gold_spec_classed)

{'2017bcc', '2018dyk', '2018gn'}

Photometry

Finally, we can also get the cleaned up photometry associated with the transients. Let’s use ['Sw J1644+57', '2018hyz', 'ASASSN-14li'] and get all of the associated photometry. This is not the fastest method but is worth the wait because you get out an astropy Table that you can then filter yourself!

phot = db.get_phot(names=['Sw J1644+57', '2018hyz', 'ASASSN-14li'])
phot

ASASSN-14li has at least one photometry point where it is unclear if a host subtraction was performed. This can be especially detrimental for UV data. Please consider filtering out UV/Optical/IR or radio rows where the corr_host column is null/None/NaN.
Sw J1644+57 has at least one photometry point where it is unclear if a host subtraction was performed. This can be especially detrimental for UV data. Please consider filtering out UV/Optical/IR or radio rows where the corr_host column is null/None/NaN.
Unable to apply the source mapping because '2021ApJ...908....4V'
2018hyz has at least one photometry point where it is unclear if a host subtraction was performed. This can be especially detrimental for UV data. Please consider filtering out UV/Optical/IR or radio rows where the corr_host column is null/None/NaN.

Table length=2301

name	converted_flux	converted_flux_err	converted_date	converted_wave	converted_freq	converted_flux_unit	converted_date_unit	converted_wave_unit	converted_freq_unit	filter_name	obs_type	upperlimit	reference	human_readable_refs	telescope
str11	float64	float64	float64	float64	float64	str7	str3	str2	str3	str9	str5	bool	object	object	str11
ASASSN-14li	15.383649548915109	0.050566574591241795	51505.66	214137470.0	1.4	mag(AB)	MJD	nm	GHz	L	radio	False	2016Sci...351...62V	van Velzen et al. (2016)	FIRST
ASASSN-14li	16.115141619232904	0.12752778976582405	59598.9	181088769.55602536	1.6555000000000002	mag(AB)	MJD	nm	GHz	L	radio	False	2024ApJ...974..241A	Anumarlapudi, Akash et al. (2024)	RACS.high
ASASSN-14li	15.25151878060192	0.08954497583865735	58597.5	337794318.8732394	0.8875	mag(AB)	MJD	nm	GHz	UHF	radio	False	2024ApJ...974..241A	Anumarlapudi, Akash et al. (2024)	RACS.low
ASASSN-14li	15.454005247629652	0.0770739780081689	59664.7	337794318.8732394	0.8875	mag(AB)	MJD	nm	GHz	UHF	radio	False	2024ApJ...974..241A	Anumarlapudi, Akash et al. (2024)	RACS.low
ASASSN-14li	15.93119819790884	0.11363406952438149	59213.0	219226660.32906762	1.3675000000000002	mag(AB)	MJD	nm	GHz	L	radio	False	2024ApJ...974..241A	Anumarlapudi, Akash et al. (2024)	RACS.mid
ASASSN-14li	15.663834434596014	0.014025899043618553	57015.689	15614190.520833332	19.200000000000003	mag(AB)	MJD	nm	GHz	K	radio	False	2016ApJ...819L..25A	Alexander et al. (2016)	VLA
ASASSN-14li	15.862890379880753	0.016848183607273505	57015.689	12236426.857142854	24.5	mag(AB)	MJD	nm	GHz	K	radio	False	2016ApJ...819L..25A	Alexander et al. (2016)	VLA
ASASSN-14li	15.69741658188068	0.014466503202056832	57028.371	59958491.59999999	5.0	mag(AB)	MJD	nm	GHz	C	radio	False	2016ApJ...819L..25A	Alexander et al. (2016)	VLA
ASASSN-14li	15.647425010840044	0.009210340371976183	57028.371	42224289.859154925	7.1	mag(AB)	MJD	nm	GHz	C	radio	False	2016ApJ...819L..25A	Alexander et al. (2016)	VLA
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2018hyz	19.83738516362267	0.005570192630460237	58996.26097222231	192.8	1554940.1348547717	mag(AB)	MJD	nm	GHz	UVW2	uvoir	False	['2020MNRAS.497.1925G']	['2020MNRAS.497.1925G']	Swift
2018hyz	28.476434655634016	12.910524835328054	58434.8762016979	0.12781876127134048	2345449564.8223705	mag(AB)	MJD	nm	GHz	0.3 - 10	xray	False	['2024ApJ...966..160G']	['2024ApJ...966..160G']	Swift
2018hyz	27.515330649116883	12.801376432258108	58456.3725319572	0.12781876127134048	2345449564.8223705	mag(AB)	MJD	nm	GHz	0.3 - 10	xray	False	['2024ApJ...966..160G']	['2024ApJ...966..160G']	Swift
2018hyz	27.81665812064398	13.826405844223709	58469.5670495012	0.12781876127134048	2345449564.8223705	mag(AB)	MJD	nm	GHz	0.3 - 10	xray	False	['2024ApJ...966..160G']	['2024ApJ...966..160G']	Swift
2018hyz	28.660940426499156	14.741498850923666	58494.4776080174	0.12781876127134048	2345449564.8223705	mag(AB)	MJD	nm	GHz	0.3 - 10	xray	False	['2024ApJ...966..160G']	['2024ApJ...966..160G']	Swift
2018hyz	28.891043419787255	16.362814126382986	58546.5206127234	0.12781876127134048	2345449564.8223705	mag(AB)	MJD	nm	GHz	0.3 - 10	xray	False	['2024ApJ...966..160G']	['2024ApJ...966..160G']	Swift
2018hyz	28.78303932129642	30.42731658485682	58668.1104212732	0.12781876127134048	2345449564.8223705	mag(AB)	MJD	nm	GHz	0.3 - 10	xray	False	['2024ApJ...966..160G']	['2024ApJ...966..160G']	Swift
2018hyz	27.805064174332202	0.0	58998.2042139734	0.12781876127134048	2345449564.8223705	mag(AB)	MJD	nm	GHz	0.3 - 10	xray	True	['2024ApJ...966..160G']	['2024ApJ...966..160G']	Swift
2018hyz	28.75285511862502	28.840901516524035	59540.323088963	0.12781876127134048	2345449564.8223705	mag(AB)	MJD	nm	GHz	0.3 - 10	xray	False	['2024ApJ...966..160G']	['2024ApJ...966..160G']	Swift
2018hyz	27.22397267261265	0.0	59684.4657037558	0.12781876127134048	2345449564.8223705	mag(AB)	MJD	nm	GHz	0.3 - 10	xray	True	['2024ApJ...966..160G']	['2024ApJ...966..160G']	Swift

We can also filter it more when we call the get_phot method by only requesting a specific observatory type from the options of radio, uvoir, or xray. So, say we just want all the radio data associated with these three objects. And, since radio data is rarely reported as AB magnitudes, lets get the flux out as a flux density in units of microjanskies. We can also request that it returns a pandas DataFrame instead of an astropy Table using the return_type='pandas' keyword.

radiophot = db.get_phot(names=['Sw J1644+57', '2018hyz', 'ASASSN-14li'], obs_type='radio',
                        flux_unit='uJy', return_type='pandas')
radiophot

ASASSN-14li has at least one photometry point where it is unclear if a host subtraction was performed. This can be especially detrimental for UV data. Please consider filtering out UV/Optical/IR or radio rows where the corr_host column is null/None/NaN.
Sw J1644+57 has at least one photometry point where it is unclear if a host subtraction was performed. This can be especially detrimental for UV data. Please consider filtering out UV/Optical/IR or radio rows where the corr_host column is null/None/NaN.
2018hyz has at least one photometry point where it is unclear if a host subtraction was performed. This can be especially detrimental for UV data. Please consider filtering out UV/Optical/IR or radio rows where the corr_host column is null/None/NaN.

	name	converted_flux	converted_flux_err	converted_date	converted_wave	converted_freq	converted_flux_unit	converted_date_unit	converted_wave_unit	converted_freq_unit	filter_name	obs_type	upperlimit	reference	human_readable_refs	telescope
0	ASASSN-14li	2550.0	140.0	51505.66	2.141375e+08	1.4000	uJy	MJD	nm	GHz	L	radio	False	2016Sci...351...62V	van Velzen et al. (2016)	FIRST
1	ASASSN-14li	1300.0	180.0	59598.90	1.810888e+08	1.6555	uJy	MJD	nm	GHz	L	radio	False	2024ApJ...974..241A	Anumarlapudi, Akash et al. (2024)	RACS.high
2	ASASSN-14li	2880.0	280.0	58597.50	3.377943e+08	0.8875	uJy	MJD	nm	GHz	UHF	radio	False	2024ApJ...974..241A	Anumarlapudi, Akash et al. (2024)	RACS.low
3	ASASSN-14li	2390.0	200.0	59664.70	3.377943e+08	0.8875	uJy	MJD	nm	GHz	UHF	radio	False	2024ApJ...974..241A	Anumarlapudi, Akash et al. (2024)	RACS.low
4	ASASSN-14li	1540.0	190.0	59213.00	2.192267e+08	1.3675	uJy	MJD	nm	GHz	L	radio	False	2024ApJ...974..241A	Anumarlapudi, Akash et al. (2024)	RACS.mid
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
123	2018hyz	1388.0	NaN	59400.64	5.995849e+07	5.0000	uJy	MJD	nm	GHz	C	radio	False	[2022ApJ...938...28C]	Cendes et al. (2022)	NaN
124	2018hyz	2939.0	NaN	59554.64	5.995849e+07	5.0000	uJy	MJD	nm	GHz	C	radio	False	[2022ApJ...938...28C]	Cendes et al. (2022)	NaN
125	2018hyz	4381.0	NaN	59627.64	5.995849e+07	5.0000	uJy	MJD	nm	GHz	C	radio	False	[2022ApJ...938...28C]	Cendes et al. (2022)	NaN
126	2018hyz	4807.0	NaN	59679.64	5.995849e+07	5.0000	uJy	MJD	nm	GHz	C	radio	False	[2022ApJ...938...28C]	Cendes et al. (2022)	NaN
127	2018hyz	7837.0	NaN	59724.64	5.450772e+07	5.5000	uJy	MJD	nm	GHz	C	radio	False	[2022ApJ...938...28C]	Cendes et al. (2022)	NaN

791 rows × 16 columns

Then we can do things like plot the light curve of all of these values!

fig, axs = plt.subplots(1,3, figsize=(18,6))

for (name, data), ax in zip(radiophot.groupby('name'), axs):

    discovery_date = db.get_meta(names=name)[0].get_discovery_date().mjd

    for (f, df) in data.groupby('filter_name'):
        fig = otter.plotter.plotter.plot_light_curve(date=df.converted_date - discovery_date,
                                             flux=df.converted_flux,
                                             flux_err=df.converted_flux_err,
                                             ax=ax,
                                             fig=fig,
                                             marker='o',
                                             linestyle='none',
                                             label=f'{name}: {f}')

    ax.legend()
    ax.set_xlim(10,4000)
    ax.set_xscale("log")
    ax.set_yscale("log")

Where the colors represent the observation frequency! Obviously these are not pubblication ready but they are at least a good way to easily visualize the data while working with it.