Hubble Source Catalog API Notebook

A new MAST interface supports queries to the current and previous versions of the Hubble Source Catalog. It allows searches of the summary table (with multi-filter mean photometry) and the detailed table (with all the multi-epoch measurements). It also has an associated API, which is used in this notebook.

This is based on HSC Use Case #3.

• It searches the HSC for variable objects in the vicinity of dwarf galaxy IC 1613,

• shows the positions of those objects in a color-magnitude diagram,

• extracts light curves for an example object, and

• displays cutout images from the Hubble observations that were used for the light curve measurements.

The whole process takes only 30 seconds to complete.

Another notebook generates a color-magnitude diagram for the Small Magellanic Cloud in only a couple of minutes. A more complex notebook that shows how to access the proper motion tables using the HSC API is also available.

Instructions:

• Complete the initialization steps described below.

• Run the notebook.

Running the notebook from top to bottom takes about 30 seconds.

Table of Contents

• Initialization

• Get metadata on available HSC columns

• Find variable objects in IC 1613

  • Use MAST name resolver

  • Search HSC summary table

  • Plot variability index versus magnitude

  • Show variable objects in a color-magnitude diagram

• Get HSC light curve for a variable

• Extract HLA cutout images for the variable

Initialization

Install Python modules

1. This notebook requires the use of Python 3.

2. Modules can be installed with conda, if using the Anaconda distribution of python, or with pip.

   • If you are using conda, do not install / update / remove a module with pip, that exists in a conda channel.

   • If a module is not available with conda, then it’s okay to install it with pip

import astropy
from astropy.coordinates import SkyCoord
import time
import requests
import numpy as np
import matplotlib.pyplot as plt

from pprint import pprint

from astropy.table import Table
import pandas as pd

from PIL import Image
from io import BytesIO, StringIO

# set width for pprint
astropy.conf.max_width = 150

# set universal matplotlib parameters
plt.rcParams.update({'font.size': 16})

MAST API functions

• Execute HSC searches and resolve names using MAST query.

• Here we define several interrelated functions for retrieving information from the MAST API.

  • The hcvcone(ra, dec, radius [, keywords]) function searches the HCV catalog near a position.

  • The hcvsearch() function performs general non-positional queries.

  • The hcvmetadata() function gives information about the columns available in a table.

hscapiurl = "https://catalogs.mast.stsci.edu/api/v0.1/hsc"


def hsccone(ra, dec, radius, table="summary", release="v3", format="csv", magtype="magaper2",
            columns=None, baseurl=hscapiurl, verbose=False, **kw):
    """Do a cone search of the HSC catalog

    Parameters
    ----------
    ra (float): (degrees) J2000 Right Ascension
    dec (float): (degrees) J2000 Declination
    radius (float): (degrees) Search radius (<= 0.5 degrees)
    table (string): summary, detailed, propermotions, or sourcepositions
    release (string): v3 or v2
    magtype (string): magaper2 or magauto (only applies to summary table)
    format: csv, votable, json, table
    columns: list of column names to include (None means use defaults)
    baseurl: base URL for the request
    verbose: print info about request
    **kw: other parameters (e.g., 'numimages.gte':2)
    """

    data = kw.copy()
    data['ra'] = ra
    data['dec'] = dec
    data['radius'] = radius
    return hscsearch(table=table, release=release, format=format, magtype=magtype,
                     columns=columns, baseurl=baseurl, verbose=verbose, **data)


def hscsearch(table="summary", release="v3", magtype="magaper2", format="csv",
              columns=None, baseurl=hscapiurl, verbose=False, **kw):
    """Do a general search of the HSC catalog (possibly without ra/dec/radius)

    Parameters
    ----------
    table (string): summary, detailed, propermotions, or sourcepositions
    release (string): v3 or v2
    magtype (string): magaper2 or magauto (only applies to summary table)
    format: csv, votable, json, table
    columns: list of column names to include (None means use defaults)
    baseurl: base URL for the request
    verbose: print info about request
    **kw: other parameters (e.g., 'numimages.gte':2).  Note this is required!
    """

    data = kw.copy()
    if not data:
        raise ValueError("You must specify some parameters for search")
    if format not in ("csv", "votable", "json", 'table'):
        raise ValueError("Bad value for format")
    if format == "table":
        rformat = "csv"
    else:
        rformat = format
    url = f"{cat2url(table, release, magtype, baseurl=baseurl)}.{rformat}"
    if columns:
        # check that column values are legal
        # create a dictionary to speed this up
        dcols = {}
        for col in hscmetadata(table, release, magtype)['name']:
            dcols[col.lower()] = 1
        badcols = []
        for col in columns:
            if col.lower().strip() not in dcols:
                badcols.append(col)
        if badcols:
            raise ValueError(f"Some columns not found in table: {', '.join(badcols)}")
        # two different ways to specify a list of column values in the API
        # data['columns'] = columns
        data['columns'] = f"[{','.join(columns)}]"

    # either get or post works
    # r = requests.post(url, data=data)
    r = requests.get(url, params=data)

    if verbose:
        print(r.url)
    r.raise_for_status()
    if format == "json":
        return r.json()
    elif format == "table":
        # use pandas to work around bug in Windows for ascii.read
        return Table.from_pandas(pd.read_csv(StringIO(r.text)))
    else:
        return r.text


def hscmetadata(table="summary", release="v3", magtype="magaper2", baseurl=hscapiurl):
    """Return metadata for the specified catalog and table
    
    Parameters
    ----------
    table (string): summary, detailed, propermotions, or sourcepositions
    release (string): v3 or v2
    magtype (string): magaper2 or magauto (only applies to summary table)
    baseurl: base URL for the request
    
    Returns an astropy table with columns name, type, description
    """
    url = f"{cat2url(table, release, magtype, baseurl=baseurl)}/metadata"
    r = requests.get(url)
    r.raise_for_status()
    v = r.json()
    # convert to astropy table
    tab = Table(rows=[(x['name'], x['type'], x['description']) for x in v],
                names=('name', 'type', 'description'))
    return tab


def cat2url(table="summary", release="v3", magtype="magaper2", baseurl=hscapiurl):
    """Return URL for the specified catalog and table
    
    Parameters
    ----------
    table (string): summary, detailed, propermotions, or sourcepositions
    release (string): v3 or v2
    magtype (string): magaper2 or magauto (only applies to summary table)
    baseurl: base URL for the request
    
    Returns a string with the base URL for this request
    """
    checklegal(table, release, magtype)
    if table == "summary":
        url = f"{baseurl}/{release}/{table}/{magtype}"
    else:
        url = f"{baseurl}/{release}/{table}"
    return url


def checklegal(table, release, magtype):
    """Checks if this combination of table, release and magtype is acceptable
    
    Raises a ValueError exception if there is problem
    """
    
    releaselist = ("v2", "v3")
    if release not in releaselist:
        raise ValueError(f"Bad value for release (must be one of {', '.join(releaselist)})")
    if release == "v2":
        tablelist = ("summary", "detailed")
    else:
        tablelist = ("summary", "detailed", "propermotions", "sourcepositions")
    if table not in tablelist:
        raise ValueError(f"Bad value for table (for {release} must be one of {', '.join(tablelist)})")
    if table == "summary":
        magtypelist = ("magaper2", "magauto")
        if magtype not in magtypelist:
            raise ValueError(f"Bad value for magtype (must be one of {', '.join(magtypelist)})")

Get metadata on available columns

The metadata query returns information on the columns in the table. It works for any of the tables in the API (summary, detailed, propermotions, sourcepositions).

Note that the summary table has a huge number of columns! Each of the 133 filter/detector combinations has 3 columns with the magnitude, median absolute deviation (MAD, a robust measure of the scatter among the measurements), and the number of independent measurements in the filter. The filter name includes a prefix for the detector (A=ACS/WFC, W3=WFC3/UVIS or WFC3/IR, W2=WFPC2) followed by the standard name of the filter. So for instance all three instruments have an F814W filter, so there are columns for A_F814W, W3_F814W, and W2_F814W.

meta = hscmetadata("summary")
print(len(meta), "columns in summary")
filterlist = meta['name'][19::3].tolist()
print(len(filterlist), "filters")
pprint(filterlist, compact=True)
meta[:19]

418 columns in summary
133 filters
['W3_BLANK', 'W2_F122M', 'W2_F160BN15', 'W2_F160BW', 'W2_F170W', 'W2_F185W',
 'W3_F200LP', 'W3_F218W', 'W2_F218W', 'W3_F225W', 'W3_FQ232N', 'W3_FQ243N',
 'W2_F255W', 'W3_F275W', 'W3_F280N', 'W3_G280', 'W2_F300W', 'W3_F300X',
 'W3_F336W', 'W2_F336W', 'W3_F343N', 'W2_F343N', 'W3_F350LP', 'W3_F373N',
 'W2_F375N', 'W3_FQ378N', 'W2_F380W', 'W3_FQ387N', 'W3_F390M', 'W2_F390N',
 'W3_F390W', 'W3_F395N', 'W3_F410M', 'W2_F410M', 'W3_FQ422M', 'A_F435W',
 'W3_FQ436N', 'W3_FQ437N', 'W2_F437N', 'W3_F438W', 'W2_F439W', 'W2_F450W',
 'W3_F467M', 'W2_F467M', 'W3_F469N', 'W2_F469N', 'A_F475W', 'W3_F475W',
 'W3_F475X', 'W3_F487N', 'W2_F487N', 'W3_FQ492N', 'A_F502N', 'W3_F502N',
 'W2_F502N', 'W3_FQ508N', 'W3_F547M', 'W2_F547M', 'A_F550M', 'A_F555W',
 'W3_F555W', 'W2_F555W', 'W2_F569W', 'W3_FQ575N', 'W2_F588N', 'W3_F600LP',
 'A_F606W', 'W3_F606W', 'W2_F606W', 'W3_FQ619N', 'W3_F621M', 'W2_F622W',
 'A_F625W', 'W3_F625W', 'W3_F631N', 'W2_F631N', 'W3_FQ634N', 'W3_F645N',
 'W3_F656N', 'W2_F656N', 'W3_F657N', 'A_F658N', 'W3_F658N', 'W2_F658N',
 'A_F660N', 'W3_F665N', 'W3_F665N_F6', 'W3_FQ672N', 'W3_F673N', 'W2_F673N',
 'W3_FQ674N', 'W2_F675W', 'W3_F680N', 'W3_F689M', 'W2_F702W', 'W3_FQ727N',
 'W3_FQ750N', 'W3_F763M', 'A_F775W', 'W3_F775W', 'W2_F785LP', 'W2_F791W',
 'A_F814W', 'W3_F814W', 'W2_F814W', 'W3_F845M', 'A_F850LP', 'W3_F850LP',
 'W2_F850LP', 'W3_FQ889N', 'W3_FQ906N', 'W3_FQ924N', 'W3_FQ937N', 'W3_F953N',
 'W2_F953N', 'W3_F098M', 'W3_G102', 'W2_F1042M', 'W3_F105W', 'W3_F110W',
 'W3_F125W', 'W3_F126N', 'W3_F127M', 'W3_F128N', 'W3_F130N', 'W3_F132N',
 'W3_F139M', 'W3_F140W', 'W3_G141', 'W3_F153M', 'W3_F160W', 'W3_F164N',
 'W3_F167N']

Table length=19

name	type	description
str16	str5	str163
MatchID	long	identifier for the match
MatchRA	float	right ascension coordinate of the match position
MatchDec	float	declination coordinate of the match position
DSigma	float	standard deviation of source positions in match
AbsCorr	char	indicator of whether the match contains sources that are aligned to a standard catalog
NumFilters	int	number of filters in match with sources detected in the aper2 aperture
NumVisits	int	number of visits in match with sources detected in the aper2 aperture
NumImages	int	number of Hubble Legacy Archive single filter, visit-combined (level 2) images in match with sources detected in the aper2 aperture
StartTime	char	earliest start time of exposures in match with sources detected in the aper2 aperture
StopTime	char	latest stop time of exposures in match with sources detected in the aper2 aperture
StartMJD	float	modified Julian date (MJD) for earliest start time of exposures in match with sources detected in the aper2 aperture
StopMJD	float	modified Julian date (MJD) for latest stop time of exposures in match with sources detected in the aper2 aperture
TargetName	char	name of a target for an exposure in match
CI	float	average normalized concentration index for sources detected in the aper2 aperture within the match
CI_Sigma	float	standard deviation of normalized concentration index values for sources detected in the aper2 aperture within the match
KronRadius	float	average Kron radius for sources detected in the aper2 aperture within the match
KronRadius_Sigma	float	standard deviation of Kron radius values for sources detected in the aper2 aperture within the match
Extinction	float	extinction, obtained from the NASA/IPAC Extragalactic Database (NED), along the line of sight to the match position
SpectrumFlag	char	Y/N indicator of whether there is a spectrum in the Hubble Legacy Archive for this match. If the value is Y, then there is an entry in table SpecCat for the match.

Find variable objects in the dwarf irregular galaxy IC 1613

This is based on HSC Use Case #3, which shows an example of selecting objects from the HSC in portal. This is simple to do using the HSC API.

Use astropy name resolver to get position of IC 1613

target = 'IC 1613'
coord_ic1613 = SkyCoord.from_name(target)

ra_ic1613 = coord_ic1613.ra.degree
dec_ic1613 = coord_ic1613.dec.degree
print(f'ra: {ra_ic1613}\ndec: {dec_ic1613}')

ra: 16.2016962
dec: 2.1194959

Select objects with enough measurements to determine variability

This searches the summary table for objects within 0.5 degrees of the galaxy center that have at least 10 measurements in both ACS F475W and F814W.

# save typing a quoted list of columns
columns = """MatchID,MatchRA,MatchDec,NumFilters,NumVisits,NumImages,StartMJD,StopMJD,
    A_F475W, A_F475W_N, A_F475W_MAD,
    A_F814W, A_F814W_N, A_F814W_MAD""".split(",")
columns = [x.strip() for x in columns]
columns = [x for x in columns if x and not x.startswith('#')]

constraints = {'A_F475W_N.gte': 10, 'A_F814W_N.gte': 10}

t0 = time.time()
tab = hsccone(ra_ic1613, dec_ic1613, 0.5, table="summary", release='v3', columns=columns, verbose=True, format="table", **constraints)
print(f"{(time.time()-t0):.1f} s: retrieved data and converted to {len(tab)}-row astropy table")

# clean up the output format
tab['A_F475W'].format = "{:.3f}"
tab['A_F475W_MAD'].format = "{:.3f}"
tab['A_F814W'].format = "{:.3f}"
tab['A_F814W_MAD'].format = "{:.3f}"
tab['MatchRA'].format = "{:.6f}"
tab['MatchDec'].format = "{:.6f}"
tab['StartMJD'].format = "{:.5f}"
tab['StopMJD'].format = "{:.5f}"
tab

https://catalogs.mast.stsci.edu/api/v0.1/hsc/v3/summary/magaper2.csv?A_F475W_N.gte=10&A_F814W_N.gte=10&ra=16.2016962&dec=2.1194959&radius=0.5&columns=%5BMatchID%2CMatchRA%2CMatchDec%2CNumFilters%2CNumVisits%2CNumImages%2CStartMJD%2CStopMJD%2CA_F475W%2CA_F475W_N%2CA_F475W_MAD%2CA_F814W%2CA_F814W_N%2CA_F814W_MAD%5D
20.8 s: retrieved data and converted to 18666-row astropy table

Table length=18666

MatchID	MatchRA	MatchDec	NumFilters	NumVisits	NumImages	StartMJD	StopMJD	A_F475W	A_F475W_N	A_F475W_MAD	A_F814W	A_F814W_N	A_F814W_MAD
int64	float64	float64	int64	int64	int64	float64	float64	float64	int64	float64	float64	int64	float64
54563554	16.122682	2.150414	2	12	24	53965.29153	53967.71941	25.930	12	0.047	25.204	12	0.030
55013249	16.114791	2.129038	2	12	24	53965.29153	53967.71941	26.086	12	0.042	26.484	12	0.061
54566052	16.140499	2.142751	2	12	24	53965.29153	53967.71941	25.478	12	0.024	24.774	12	0.031
54574280	16.102370	2.162465	3	14	26	53965.29153	57009.86262	25.364	12	0.030	24.725	12	0.018
54576017	16.120919	2.144474	3	14	26	53965.29153	57009.86262	25.989	12	0.017	25.132	12	0.021
54678903	16.126962	2.192171	2	12	24	53965.29153	53967.71941	26.141	12	0.029	25.199	12	0.023
54679910	16.112520	2.162524	3	14	26	53965.29153	57009.86262	25.921	12	0.020	25.187	12	0.017
54694774	16.099953	2.171509	3	14	26	53965.29153	57009.86262	25.373	12	0.021	24.618	12	0.014
54119712	16.130034	2.192925	2	12	24	53965.29153	53967.71941	26.293	12	0.034	26.701	12	0.087
...	...	...	...	...	...	...	...	...	...	...	...	...	...
107815376	16.133519	2.174931	2	12	24	53965.29153	53967.71941	26.146	12	0.042	26.172	12	0.021
108051851	16.128325	2.149198	3	14	26	53965.29153	57009.86262	26.445	12	0.063	25.702	12	0.061
108054963	16.095892	2.173002	2	12	24	53965.29153	53967.71941	23.380	12	0.014	21.612	12	0.010
108055849	16.117681	2.131627	2	12	24	53965.29153	53967.71941	26.614	12	0.034	25.778	12	0.053
108066368	16.135327	2.139317	2	12	24	53965.29153	53967.71941	25.397	12	0.030	25.991	12	0.057
108069445	16.145261	2.162300	2	12	24	53965.29153	53967.71941	25.920	12	0.047	25.337	12	0.028
107569426	16.142839	2.156335	2	12	24	53965.29153	53967.71941	25.931	12	0.028	26.464	12	0.057
108075108	16.124695	2.172665	2	12	24	53965.29153	53967.71941	26.045	12	0.030	26.320	12	0.044
108083778	16.104882	2.157902	2	12	24	53965.29153	53967.71941	25.858	12	0.074	25.443	12	0.020
108088475	16.101156	2.125474	2	12	24	53965.29153	53967.71941	26.786	12	0.056	26.107	12	0.045

Plot object positions on the sky

We mark the galaxy center as well. Note that this field is in the outskirts of IC 1613. The 0.5 search radius (which is the maximum allowed in the API) allows finding these objects.

fig, ax = plt.subplots(figsize=(10, 10))
ax.plot('MatchRA', 'MatchDec', 'bo', markersize=1, data=tab, label=f'{len(tab)} HSC measurements')
ax.plot(ra_ic1613, dec_ic1613, 'rx', label=target, markersize=10)
ax.set(xlabel='RA [deg]', ylabel='Dec [deg]', aspect='equal')
ax.invert_xaxis()
_ = ax.legend(loc='best')

Plot MAD variability index versus magnitude in F475W

The median absolute deviation is measured among the ~12 magnitude measurements in the catalog. Some scatter is expected from noise (which increases for fainter objects). Objects with MAD values that are high are likely to be variable.

Select variable objects that are not too faint.

wvar = np.where((tab['A_F475W_MAD'] > 0.1) & (tab['A_F475W'] < 24) & (tab['A_F475W'] > 21))[0]

fig, ax = plt.subplots(figsize=(10, 10))
ax.plot('A_F475W', 'A_F475W_MAD', 'bo', markersize=2, alpha=0.1, data=tab,
        label=f'{len(tab)} HSC measurements near {target}')
ax.plot('A_F475W', 'A_F475W_MAD', 'ro', markersize=5, data=tab[wvar],
        label=f'{len(wvar)} variable candidates')
ax.set(xlabel='A_F475W [mag]', ylabel='A_F475W_MAD [mag]')
_ = ax.legend(loc='best')

Check positions of variable objects in a color-magnitude diagram

Note that these objects are generally located in the Cepheid instability strip.

b_minus_i = tab['A_F475W'] - tab['A_F814W']

fig, ax = plt.subplots(figsize=(10, 10))
ax.plot(b_minus_i, tab['A_F475W'], 'bo', markersize=2, alpha=0.1,
        label=f'{len(tab)} HSC measurements near {target}')
ax.plot(b_minus_i[wvar], tab['A_F475W'][wvar], 'ro', markersize=5,
        label=f'{len(wvar)} variable candidates')
ax.set(xlabel='A_F475W - A_F814W [mag]', ylabel='A_F475W [mag]')
ax.invert_yaxis()
_ = ax.legend(loc='best')

Query the API for the light curve for one of the objects

Select the most variable object as an example.

wvar = wvar[np.argsort(-tab['A_F475W_MAD'][wvar])]
iselect = wvar[0]
print(f"MatchID {tab['MatchID'][iselect]} B = {tab['A_F475W'][iselect]:.3f} B-I = {b_minus_i[iselect]:.3f}")
tab[wvar]

MatchID 80189155 B = 22.451 B-I = 0.450

Table length=29

MatchID	MatchRA	MatchDec	NumFilters	NumVisits	NumImages	StartMJD	StopMJD	A_F475W	A_F475W_N	A_F475W_MAD	A_F814W	A_F814W_N	A_F814W_MAD
int64	float64	float64	int64	int64	int64	float64	float64	float64	int64	float64	float64	int64	float64
80189155	16.140740	2.155288	3	14	26	53965.29153	57009.86262	22.451	12	0.294	22.001	12	0.198
8200488	16.144772	2.152338	3	14	26	53965.29153	57009.86262	22.202	12	0.260	21.768	12	0.188
66012678	16.135134	2.154740	3	14	26	53965.29153	57009.86262	22.559	12	0.253	22.101	12	0.192
63078526	16.105513	2.155580	3	14	26	53965.29153	57009.86262	23.317	12	0.227	22.897	12	0.138
89909862	16.117180	2.168810	3	14	26	53965.29153	57009.86262	22.733	12	0.224	22.545	12	0.099
92119358	16.136563	2.182817	2	12	24	53965.29153	53967.71941	23.347	12	0.213	23.234	12	0.131
66980564	16.125611	2.135287	3	14	26	53965.29153	57009.86262	22.732	12	0.194	22.365	12	0.138
95722356	16.133030	2.175638	3	14	26	53965.29153	57009.86262	23.359	12	0.180	22.921	12	0.129
68356083	16.095709	2.147460	2	12	24	53965.29153	53967.71941	23.378	12	0.176	22.981	12	0.109
...	...	...	...	...	...	...	...	...	...	...	...	...	...
69790109	16.141284	2.152881	3	14	26	53965.29153	57009.86262	22.635	12	0.133	22.138	12	0.071
100437228	16.111310	2.160908	3	14	26	53965.29153	57009.86262	23.382	12	0.123	22.914	12	0.070
30855111	16.105925	2.157603	3	14	26	53965.29153	57009.86262	22.781	12	0.117	22.698	12	0.056
70217468	16.107142	2.178161	3	14	26	53965.29153	57009.86262	23.804	12	0.116	23.428	12	0.116
9510698	16.137963	2.141027	3	14	26	53965.29153	57009.86262	23.343	12	0.114	23.101	12	0.080
13317380	16.118322	2.152817	3	14	26	53965.29153	57009.86262	23.599	12	0.109	23.169	12	0.091
17240899	16.139667	2.153597	3	14	26	53965.29153	57009.86262	22.761	12	0.105	22.195	12	0.073
105545986	16.144336	2.156262	3	14	26	53965.29153	57009.86262	23.160	12	0.104	22.941	12	0.138
66716166	16.132260	2.163512	3	14	26	53965.29153	57009.86262	23.075	12	0.104	22.864	12	0.075
63755650	16.095356	2.137338	2	12	24	53965.29153	53967.71941	22.729	12	0.103	22.442	12	0.062

Get column metadata for detailed observation table (which has time-dependent magnitudes).

meta = hscmetadata("detailed")
print(len(meta), "columns in detailed")
pprint(meta['name'].tolist(), compact=True)

39 columns in detailed
['CatID', 'MatchID', 'MemID', 'SourceID', 'ImageID', 'Det', 'MatchRA',
 'MatchDec', 'SourceRA', 'SourceDec', 'D', 'DSigma', 'AbsCorr', 'XImage',
 'YImage', 'ImageName', 'Instrument', 'Mode', 'Detector', 'Aperture',
 'ExposureTime', 'StartTime', 'StopTime', 'StartMJD', 'StopMJD', 'WaveLength',
 'Filter', 'TargetName', 'FluxAper2', 'MagAper2', 'MagAuto', 'PropID', 'CI',
 'KronRadius', 'Flags', 'HTMID', 'X', 'Y', 'Z']

Get separate light curves for F475W and F814W from the detailed table

columns = """MatchID,SourceID,StartMJD,Detector,Filter,MagAper2,Flags,ImageName""".split(",")
columns = [x.strip() for x in columns]
columns = [x for x in columns if x and not x.startswith('#')]

constraints = {'MatchID': tab['MatchID'][iselect], 'Detector': 'ACS/WFC'}
t0 = time.time()
f475 = hscsearch(table="detailed", release='v3', columns=columns, Filter='F475W', format="table", **constraints)
f814 = hscsearch(table="detailed", release='v3', columns=columns, Filter='F814W', format="table", **constraints)
print(f"{time.time()-t0:.1f} s: retrieved data and converted to {len(f475)} (F475W) and {len(f814)} (F814W) row astropy tables")

f475.sort('StartMJD')
f814.sort('StartMJD')
f475['MagAper2'].format = "{:.3f}"
f475['StartMJD'].format = "{:.5f}"
f814['MagAper2'].format = "{:.3f}"
f814['StartMJD'].format = "{:.5f}"

f475

1.7 s: retrieved data and converted to 12 (F475W) and 12 (F814W) row astropy tables

Table length=12

MatchID	SourceID	StartMJD	Detector	Filter	MagAper2	Flags	ImageName
int64	int64	float64	str7	str5	float64	int64	str26
80189155	4000999062305	53965.29153	ACS/WFC	F475W	22.192	0	hst_10505_07_acs_wfc_f475w
80189155	4000992888001	53965.42473	ACS/WFC	F475W	22.246	0	hst_10505_08_acs_wfc_f475w
80189155	4001095116962	53965.55794	ACS/WFC	F475W	22.376	0	hst_10505_09_acs_wfc_f475w
80189155	4000922680735	53965.69115	ACS/WFC	F475W	22.552	0	hst_10505_10_acs_wfc_f475w
80189155	4001042276145	53966.29057	ACS/WFC	F475W	22.705	0	hst_10505_11_acs_wfc_f475w
80189155	4001225221390	53966.42377	ACS/WFC	F475W	22.802	0	hst_10505_12_acs_wfc_f475w
80189155	4000895760719	53966.55698	ACS/WFC	F475W	22.779	0	hst_10505_13_acs_wfc_f475w
80189155	4000814317863	53966.69019	ACS/WFC	F475W	22.801	0	hst_10505_14_acs_wfc_f475w
80189155	4000830929643	53967.22301	ACS/WFC	F475W	22.526	0	hst_10505_15_acs_wfc_f475w
80189155	4000859171115	53967.35622	ACS/WFC	F475W	21.522	0	hst_10505_16_acs_wfc_f475w
80189155	4001162870082	53967.48942	ACS/WFC	F475W	21.449	0	hst_10505_17_acs_wfc_f475w
80189155	4000926592856	53967.62263	ACS/WFC	F475W	21.769	0	hst_10505_18_acs_wfc_f475w

Plot the light curves

The light curves appear well-behaved and are closely correlated in the two filters.

fig, ax = plt.subplots(figsize=(10, 6), tight_layout=True)

ax.plot('StartMJD', 'MagAper2', 'bo', data=f475, label='ACS/WFC F475W')
ax.plot('StartMJD', 'MagAper2', 'ro', data=f814, label='ACS/WFC F814W')

ax.set(xlabel='MJD [days]', ylabel='[mag]')
ax.invert_yaxis()
ax.legend()

<matplotlib.legend.Legend at 0x7f4510d8aa90>

Extract HLA cutout images for the F475W images

Get HLA F475W cutout images for the example variable. The get_hla_cutout function reads a single cutout image (as a JPEG grayscale image) and returns a PIL image object. See the documentation on the fitscut image cutout service for more information on the web service being used.

Examination of the images can be useful to identified cosmic-ray contamination and other possible image artifacts. In this case, no issues are seen, so the light curve is likely to be reliable.

def get_hla_cutout(imagename, ra, dec, size=33, autoscale=99.5, asinh=1, zoom=1):
    
    """Get JPEG cutout for an image"""
    
    url = "https://hla.stsci.edu/cgi-bin/fitscut.cgi"
    r = requests.get(url, params=dict(ra=ra, dec=dec, size=size, format="jpeg",
                                      red=imagename, autoscale=autoscale, asinh=asinh, zoom=zoom))
    im = Image.open(BytesIO(r.content))
    return im

# sort images by magnitude from faintest to brightest
isort = np.argsort(-f475['MagAper2'])

imagename = f475['ImageName'][isort]
mag = f475['MagAper2'][isort]
mjd = f475['StartMJD'][isort]

nim = len(imagename)
ncols = 4 # images per row
nrows = (nim+ncols-1)//ncols

imsize = 15
mra = tab['MatchRA'][iselect]
mdec = tab['MatchDec'][iselect]

# download list of images; might take a minute
images = [get_hla_cutout(imagename[k], mra, mdec, size=imsize) for k in range(nim)]

plt.rcParams.update({"font.size": 11})
fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(15, (15/ncols)*nrows), tight_layout=True)

axes = axes.flat

for i, (ax, img) in enumerate(zip(axes, images)):
    ax.imshow(img, origin="upper", cmap="gray")
    ax.set_title(f'{mjd[i]:.5f} f475w={mag[i]:.3f}')

About this Notebook

Authors: Rick White, Trenton McKinney
Keywords: HSC, catalogs
Last Updated: October 2024

For support, please contact the Archive HelpDesk at archive@stsci.edu.

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