A series of queries folks have performed for research or for kicks.
This illustrates querying Vizier with specific keyword, and the use of astropy.coordinates to describe a query. Vizier’s keywords can indicate wavelength & object type, although only object type is shown here.
>>> from astroquery.vizier import Vizier
>>> v = Vizier(keywords=['stars:white_dwarf'])
>>> from astropy import coordinates
>>> from astropy import units as u
>>> c = coordinates.SkyCoord(0,0,unit=('deg','deg'),frame='icrs')
>>> result = v.query_region(c, radius=2*u.deg)
>>> print len(result)
31
>>> result[0].pprint()
LP Rem Name RA1950 DE1950 Rmag l_Pmag Pmag u_Pmag spClass pm pmPA _RA.icrs _DE.icrs
-------- --- ---- -------- -------- ---- ------ ---- ------ ------- ------ ---- -------- --------
584-0063 00 03 23 +00 01.8 18.1 18.3 f 0.219 93 00 05 57 +00 18.7
643-0083 23 50 40 +00 33.4 15.9 17.0 k 0.197 93 23 53 14 +00 50.3
584-0030 23 54 05 -01 32.3 16.6 17.7 k 0.199 193 23 56 39 -01 15.4
This illustrates adding new output fields to SIMBAD queries. Run list_votable_fields to get the full list of valid fields.
>>> from astroquery.simbad import Simbad
>>> s = Simbad()
>>> # bibcodelist(date1-date2) lists the number of bibliography
>>> # items referring to each object over that date range
>>> s.add_votable_fields('bibcodelist(2003-2013)')
>>> r = s.query_object('m31')
>>> r.pprint()
MAIN_ID RA DEC RA_PREC DEC_PREC COO_ERR_MAJA COO_ERR_MINA COO_ERR_ANGLE COO_QUAL COO_WAVELENGTH COO_BIBCODE BIBLIST_2003_2013
------- ------------ ------------ ------- -------- ------------ ------------ ------------- -------- -------------- ------------------- -----------------
M 31 00 42 44.330 +41 16 07.50 7 7 nan nan 0 B I 2006AJ....131.1163S 3758
This illustrates finding the spectral type of some particular star.
>>> from astroquery.simbad import Simbad
>>> customSimbad = Simbad()
>>> customSimbad.add_votable_fields('sptype')
>>> result = customSimbad.query_object('g her')
>>> result['MAIN_ID'][0]
'V* g Her'
>>> result['SP_TYPE'][0]
'M6III'
>>> from astroquery.simbad import Simbad
>>> customSimbad = Simbad()
>>> # We've seen errors where ra_prec was NAN, but it's an int: that's a problem
>>> # this is a workaround we adapted
>>> customSimbad.add_votable_fields('ra(d)','dec(d)')
>>> customSimbad.remove_votable_fields('coordinates')
>>> from astropy import coordinates
>>> C = coordinates.SkyCoord(0,0,unit=('deg','deg'), frame='icrs')
>>> result = customSimbad.query_region(C, radius='2 degrees')
>>> result[:5].pprint()
MAIN_ID RA_d DEC_d
------------- ----------- ------------
ALFALFA 5-186 0.00000000 0.00000000
ALFALFA 5-188 0.00000000 0.00000000
ALFALFA 5-206 0.00000000 0.00000000
ALFALFA 5-241 0.00000000 0.00000000
ALFALFA 5-293 0.00000000 0.00000000
This illustrates a simple usage of the open_exoplanet_catalogue module.
Finding the mass of a specific planet:
>>> from astroquery import open_exoplanet_catalogue as oec
>>> from astroquery.open_exoplanet_catalogue import findvalue
>>> cata = oec.get_catalogue()
>>> kepler68b = cata.find(".//planet[name='Kepler-68 b']")
>>> print findvalue( kepler68b, 'mass')
0.02105109
Grab some data from ALMA, then analyze it using the Spectral Cube package after identifying some spectral lines in the data.
from astroquery.alma import Alma
from astroquery.splatalogue import Splatalogue
from astroquery.simbad import Simbad
from astropy import units as u
from astropy import constants
from spectral_cube import SpectralCube
m83table = Alma.query_object('M83', public=True)
m83urls = Alma.stage_data(m83table['Asdm_uid'])
# Sometimes there can be duplicates: avoid them with
# list(set())
m83files = Alma.download_and_extract_files(list(set(m83urls['URL'])))
m83files = m83files
Simbad.add_votable_fields('rvel')
m83simbad = Simbad.query_object('M83')
rvel = m83simbad['RVel_Rvel'][0]*u.Unit(m83simbad['RVel_Rvel'].unit)
for fn in m83files:
if 'line' in fn:
cube = SpectralCube.read(fn)
# Convert frequencies to their rest frequencies
frange = u.Quantity([cube.spectral_axis.min(),
cube.spectral_axis.max()]) * (1+rvel/constants.c)
# Query the top 20 most common species in the frequency range of the
# cube with an upper energy state <= 50K
lines = Splatalogue.query_lines(frange[0], frange[1], top20='top20',
energy_max=50, energy_type='eu_k',
only_NRAO_recommended=True)
lines.pprint()
# Change the cube coordinate system to be in velocity with respect
# to the rest frequency (in the M83 rest frame)
rest_frequency = lines['Freq-GHz'][0]*u.GHz / (1+rvel/constants.c)
vcube = cube.with_spectral_unit(u.km/u.s,
rest_value=rest_frequency,
velocity_convention='radio')
# Write the cube with the specified line name
fmt = "{Species}{Resolved QNs}"
row = lines[0]
linename = fmt.format(**dict(zip(row.colnames,row.data)))
vcube.write('M83_ALMA_{linename}.fits'.format(linename=linename))
Find ALMA pointings that have been observed toward M83, then overplot the various fields-of view on a 2MASS image retrieved from SkyView. See http://nbviewer.ipython.org/gist/keflavich/19175791176e8d1fb204 for the notebook.
# coding: utf-8
# Querying ALMA archive for M83 pointings and plotting them on a 2MASS image
# In[1]:
from astroquery.alma import Alma
from astroquery.skyview import SkyView
import string
from astropy import units as u
import pylab as pl
import aplpy
# Retrieve M83 2MASS K-band image:
# In[2]:
m83_images = SkyView.get_images(position='M83', survey=['2MASS-K'], pixels=1500)
# Retrieve ALMA archive information *including* private data and non-science fields:
#
# In[3]:
m83 = Alma.query_object('M83', public=False, science=False)
# In[4]:
m83
# Parse components of the ALMA data. Specifically, find the frequency support - the frequency range covered - and convert that into a central frequency for beam radius estimation.
# In[5]:
def parse_frequency_support(frequency_support_str):
supports = frequency_support_str.split("U")
freq_ranges = [(float(sup.strip('[] ').split("..")[0]),
float(sup.strip('[] ').split("..")[1].split(',')[0].strip(string.letters)))
*u.Unit(sup.strip('[] ').split("..")[1].split(',')[0].strip(string.punctuation+string.digits))
for sup in supports]
return u.Quantity(freq_ranges)
def approximate_primary_beam_sizes(frequency_support_str):
freq_ranges = parse_frequency_support(frequency_support_str)
beam_sizes = [(1.22*fr.mean().to(u.m, u.spectral())/(12*u.m)).to(u.arcsec,
u.dimensionless_angles())
for fr in freq_ranges]
return u.Quantity(beam_sizes)
# In[6]:
primary_beam_radii = [approximate_primary_beam_sizes(row['Frequency_support']) for row in m83]
# Compute primary beam parameters for the public and private components of the data for plotting below
# In[7]:
private_circle_parameters = [(row['RA'],row['Dec'],np.mean(rad).to(u.deg).value)
for row,rad in zip(m83, primary_beam_radii)
if row['Release_date']!='']
public_circle_parameters = [(row['RA'],row['Dec'],np.mean(rad).to(u.deg).value)
for row,rad in zip(m83, primary_beam_radii)
if row['Release_date']=='']
unique_private_circle_parameters = np.array(list(set(private_circle_parameters)))
unique_public_circle_parameters = np.array(list(set(public_circle_parameters)))
print("PUBLIC: Number of rows: {0}. Unique pointings: {1}".format(len(m83), len(unique_public_circle_parameters)))
print("PRIVATE: Number of rows: {0}. Unique pointings: {1}".format(len(m83), len(unique_private_circle_parameters)))
# Show all of the private observation pointings that have been acquired
# In[8]:
fig = aplpy.FITSFigure(m83_images[0])
fig.show_grayscale(stretch='arcsinh')
fig.show_circles(unique_private_circle_parameters[:,0],
unique_private_circle_parameters[:,1],
unique_private_circle_parameters[:,2],
color='r', alpha=0.2)
# In principle, all of the pointings shown below should be downloadable from the archive:
# In[9]:
fig = aplpy.FITSFigure(m83_images[0])
fig.show_grayscale(stretch='arcsinh')
fig.show_circles(unique_public_circle_parameters[:,0],
unique_public_circle_parameters[:,1],
unique_public_circle_parameters[:,2],
color='b', alpha=0.2)