Preparing a proposal

The Story: Suppose that you are preparing to write a proposal on NGC1365, aiming to investigate the intriguing black hole spin this galaxy with Chandra grating observations (see: https://www.space.com/19980-monster-black-hole-spin-discovery.html )

In writing proposals, there are often the same tasks that are required: including finding and analyzing previous observations of the proposal, and creating figures that include, e.g., multiwavelength images and spectrum for the source.

# As a hint, we include the code block for Python modules that you will likely need to import:   
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline  

# For downloading files
from astropy.utils.data import download_file
from astropy.io import fits

import pyvo as vo

## There are a number of relatively unimportant warnings that 
## show up, so for now, suppress them:
import warnings
warnings.filterwarnings("ignore", module="astropy.io.votable.*")
warnings.filterwarnings("ignore", module="pyvo.utils.xml.*")

Step 1: Find out what the previously quoted Chandra 2-10 keV flux of the central source is for NGC 1365.

Hint: Do a Registry search for tables served by the HEASARC (where high energy data are archived) to find potential table with this information

#  This gets all services matching, which should be a list of only one:
tap_services=vo.regsearch(servicetype='table',keywords=['heasarc'])
#  This fetches the list of tables that this service serves:
heasarc_tables=tap_services[0].service.tables

Hint: The Chansngcat ( https://heasarc.gsfc.nasa.gov/W3Browse/chandra/chansngcat.html ) table is likely the best table. Create a table with ra, dec, exposure time, and flux (and flux errors) from the public.chansngcat catalog for Chandra observations matched within 0.1 degree.

for tablename in heasarc_tables.keys():
    if "chansng" in tablename:  
        print("Table {} has columns={}\n".format(
            tablename,
            sorted([k.name for k in heasarc_tables[tablename].columns ])))

Table chansngcat has columns=['"__row"', '"__x_ra_dec"', '"__y_ra_dec"', '"__z_ra_dec"', '"distance"', 'agn_flag', 'bii', 'class', 'dec', 'exposure', 'fit_chi_squared', 'fit_dof', 'flux', 'flux_lower', 'flux_upper', 'hardness_ratio_0', 'hardness_ratio_0_error', 'hardness_ratio_1', 'hardness_ratio_1_error', 'hardness_ratio_2', 'hardness_ratio_2_error', 'hubble_type', 'instrument', 'lii', 'log_blackhole_mass', 'log_blackhole_mass_lower', 'log_blackhole_mass_upper', 'log_l_cpt1', 'log_l_cpt2', 'log_l_halpha', 'log_l_halpha_flag', 'log_lx', 'log_lx_lower', 'log_lx_note', 'log_lx_upper', 'name', 'nh', 'nh_from_hr', 'nh_from_hr_lower', 'nh_from_hr_upper', 'nh_gal', 'nh_lower', 'nh_upper', 'nuclear_class', 'obsid', 'off_set', 'ra', 'ref_spectrum', 'sigma', 'sigma_error', 'source_number', 'spectral_index', 'spectral_index_lower', 'spectral_index_upper', 'spectral_model', 'spectral_norm_cpt1', 'spectral_norm_cpt2', 'temperature', 'temperature_lower', 'temperature_upper']

# Get the coordinate for NGC 1365
import astropy.coordinates as coord
pos=coord.SkyCoord.from_name("ngc1365")

# Construct a query that will get the ra, dec, exposure time, flux, and flux errors 
#  from this catalog in the region around this source:
query="""SELECT ra, dec, exposure, flux, flux_lower, flux_upper FROM public.chansngcat as cat 
    where contains(point('ICRS',cat.ra,cat.dec),circle('ICRS',{},{},0.1))=1 
    and cat.exposure > 0 order by cat.exposure""".format(pos.ra.deg, pos.dec.deg)
#  Submit the query.  (See the CS_Catalog_queries.ipynb for
#    information about these two search options.)
results=tap_services[0].service.run_async(query)
#results=tap_services[0].search(query)
#  Look at the results
results.to_table()

Table length=1

ra	dec	exposure	flux	flux_lower	flux_upper
float64	float64	float64	float64	float64	float64
53.4019	-36.1407	15500.0	0.0	0.0	0.0

Step 2: Make Images:

Create ultraviolet and X-ray images

Hint: Start by checking what UV image services exist (e.g., GALEX?)

## Note that to browse the columns, use the .to_table() method
uv_services=vo.regsearch(servicetype='image',keywords='galex', waveband='uv')
uv_services.to_table()['ivoid','short_name']

Table length=5

ivoid	short_name
object	object
ivo://archive.stsci.edu/sia/galex	GALEX
ivo://irsa.ipac/spitzer/images/lvl	LVL
ivo://irsa.ipac/wise/images/z0mgs	z0MGS
ivo://mast.stsci/siap/galex_atlas	GALEX_Atlas
ivo://nasa.heasarc/skyview/galex	GALEX

The keyword search for 'galex' returned a bunch of things that may have mentioned it, but let's just use the ones that have GALEX as their short name:

uv_services.to_table()[
    np.array(['GALEX' in u.short_name for u in uv_services])
    ]['ivoid', 'short_name', 'access_url']

Table length=3

ivoid	short_name	access_url
object	object	object
ivo://archive.stsci.edu/sia/galex	GALEX	https://masttest.stsci.edu/portal_tom/Mashup/VoQuery.asmx/SiaV1?MISSION=GALEX&
ivo://mast.stsci/siap/galex_atlas	GALEX_Atlas	http://archive.stsci.edu/siap/search.php?id=galex_atlas&
ivo://nasa.heasarc/skyview/galex	GALEX	https://skyview.gsfc.nasa.gov/cgi-bin/vo/sia.pl?survey=galex&

Though using the result as an Astropy Table makes it easier to look at the contents, to call the service itself, we cannot use the row of that table. You have to use the entry in the service result list itself. So use the table to browse, but select the list of services itself using the properties that have been defined as attributes such as short_name and ivoid:

galex_stsci=[s for s in uv_services if 'GALEX' in s.short_name and 'stsci' in s.ivoid][0]
galex_heasarc=[s for s in uv_services if 'GALEX' in s.short_name and 'heasarc' in s.ivoid][0]

Hint: Next create a UV image for the source

# Do an image search for NGC 1365 in the UV service found above
im_table_stsci=galex_stsci.search(pos=pos,size=0.1)
im_table_stsci.to_table()

Table length=174

collection	insname	name	trgPosRA	trgPosDec	contentlength	midpoint	naxes	naxis	scale	contenttype	coordFrame	projection	crpix	crval	cdmatrix	enremband	enrUnits	enrValue	enrMax	enrMin	accessURL
object	object	object	float64	float64	int32	float64	int32	object	object	object	object	str3	object	object	object	object	object	float64	float64	float64	object
GALEX	GALEX	nga_ngc1365-nd-int.fits.gz	53.3543381647092	-36.1370016988435	16510788	53324.2563310185	2	[3840 3840]	[0.000416667 0.000416667]	image/fits	ICRS	TAN	[1920.5 1920.5]	[53.3543 -36.137]	[-0.000416667 -0.0 -0.0 0.000416667]	UV	meters	2.35e-07	3.007e-07	1.693e-07	http://galex.stsci.edu/data/GR6/pipe/01-vsn/05057-NGA_NGC1365/d/01-main/0001-img/07-try/NGA_NGC1365-nd-int.fits.gz
GALEX	GALEX	nga_ngc1365-xd-int_2color_medium_annot.jpg	53.3543381647092	-36.1370016988435	557843	53324.2563310185	2	[3840 3840]	[0.000416667 0.000416667]	image/jpeg	ICRS	TAN	[1920.5 1920.5]	[53.3543 -36.137]	[-0.000416667 -0.0 -0.0 0.000416667]	UV	meters	2.35e-07	3.007e-07	1.693e-07	http://galex.stsci.edu/data/GR6/pipe/01-vsn/05057-NGA_NGC1365/d/01-main/0001-img/07-try/qa/NGA_NGC1365-xd-int_2color_medium_annot.jpg
GALEX	GALEX	nga_ngc1365-xd-int_2color.jpg	53.3543381647092	-36.1370016988435	1933453	53324.2563310185	2	[3840 3840]	[0.000416667 0.000416667]	image/jpeg	ICRS	TAN	[1920.5 1920.5]	[53.3543 -36.137]	[-0.000416667 -0.0 -0.0 0.000416667]	UV	meters	2.35e-07	3.007e-07	1.693e-07	http://galex.stsci.edu/data/GR6/pipe/01-vsn/05057-NGA_NGC1365/d/01-main/0001-img/07-try/qa/NGA_NGC1365-xd-int_2color.jpg
GALEX	GALEX	nga_ngc1365-xd-int_2color_large.jpg	53.3543381647092	-36.1370016988435	362982	53324.2563310185	2	[3840 3840]	[0.000416667 0.000416667]	image/jpeg	ICRS	TAN	[1920.5 1920.5]	[53.3543 -36.137]	[-0.000416667 -0.0 -0.0 0.000416667]	UV	meters	2.35e-07	3.007e-07	1.693e-07	http://galex.stsci.edu/data/GR6/pipe/01-vsn/05057-NGA_NGC1365/d/01-main/0001-img/07-try/qa/NGA_NGC1365-xd-int_2color_large.jpg
GALEX	GALEX	nga_ngc1365-xd-int_2color_medium.jpg	53.3543381647092	-36.1370016988435	470661	53324.2563310185	2	[3840 3840]	[0.000416667 0.000416667]	image/jpeg	ICRS	TAN	[1920.5 1920.5]	[53.3543 -36.137]	[-0.000416667 -0.0 -0.0 0.000416667]	UV	meters	2.35e-07	3.007e-07	1.693e-07	http://galex.stsci.edu/data/GR6/pipe/01-vsn/05057-NGA_NGC1365/d/01-main/0001-img/07-try/qa/NGA_NGC1365-xd-int_2color_medium.jpg
GALEX	GALEX	nga_ngc1365-xd-int_2color_small.jpg	53.3543381647092	-36.1370016988435	123900	53324.2563310185	2	[3840 3840]	[0.000416667 0.000416667]	image/jpeg	ICRS	TAN	[1920.5 1920.5]	[53.3543 -36.137]	[-0.000416667 -0.0 -0.0 0.000416667]	UV	meters	2.35e-07	3.007e-07	1.693e-07	http://galex.stsci.edu/data/GR6/pipe/01-vsn/05057-NGA_NGC1365/d/01-main/0001-img/07-try/qa/NGA_NGC1365-xd-int_2color_small.jpg
GALEX	GALEX	nga_ngc1365-xd-int_2color.jpg	53.3543381647092	-36.1370016988435	1933453	53328.4984143519	2	[3840 3840]	[0.000416667 0.000416667]	image/jpeg	ICRS	TAN	[1920.5 1920.5]	[53.3543 -36.137]	[-0.000416667 -0.0 -0.0 0.000416667]	UV	meters	1.573e-07	1.806e-07	1.34e-07	http://galex.stsci.edu/data/GR6/pipe/01-vsn/05057-NGA_NGC1365/d/01-main/0001-img/07-try/qa/NGA_NGC1365-xd-int_2color.jpg
GALEX	GALEX	nga_ngc1365-xd-int_2color_large.jpg	53.3543381647092	-36.1370016988435	362982	53328.4984143519	2	[3840 3840]	[0.000416667 0.000416667]	image/jpeg	ICRS	TAN	[1920.5 1920.5]	[53.3543 -36.137]	[-0.000416667 -0.0 -0.0 0.000416667]	UV	meters	1.573e-07	1.806e-07	1.34e-07	http://galex.stsci.edu/data/GR6/pipe/01-vsn/05057-NGA_NGC1365/d/01-main/0001-img/07-try/qa/NGA_NGC1365-xd-int_2color_large.jpg
GALEX	GALEX	nga_ngc1365-xd-int_2color_medium.jpg	53.3543381647092	-36.1370016988435	470661	53328.4984143519	2	[3840 3840]	[0.000416667 0.000416667]	image/jpeg	ICRS	TAN	[1920.5 1920.5]	[53.3543 -36.137]	[-0.000416667 -0.0 -0.0 0.000416667]	UV	meters	1.573e-07	1.806e-07	1.34e-07	http://galex.stsci.edu/data/GR6/pipe/01-vsn/05057-NGA_NGC1365/d/01-main/0001-img/07-try/qa/NGA_NGC1365-xd-int_2color_medium.jpg
GALEX	GALEX	nga_ngc1365-xd-int_2color_small.jpg	53.3543381647092	-36.1370016988435	123900	53328.4984143519	2	[3840 3840]	[0.000416667 0.000416667]	image/jpeg	ICRS	TAN	[1920.5 1920.5]	[53.3543 -36.137]	[-0.000416667 -0.0 -0.0 0.000416667]	UV	meters	1.573e-07	1.806e-07	1.34e-07	http://galex.stsci.edu/data/GR6/pipe/01-vsn/05057-NGA_NGC1365/d/01-main/0001-img/07-try/qa/NGA_NGC1365-xd-int_2color_small.jpg
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
GALEX	GALEX	ais_423_0002_sg49-xd-int_2color.jpg	53.9452301043047	-36.1117650864775	3487526	54460.4016261574	2	[3840 3840]	[0.000416667 0.000416667]	image/jpeg	ICRS	TAN	[1920.5 1920.5]	[53.9452 -36.1118]	[-0.000416667 -0.0 -0.0 0.000416667]	UV	meters	2.35e-07	3.007e-07	1.693e-07	http://galex.stsci.edu/data/GR6/pipe/02-vsn/50423-AIS_423/d/00-visits/0002-img/07-try/qa/AIS_423_0002_sg49-xd-int_2color.jpg
GALEX	GALEX	ais_423_0002_sg49-xd-int_2color_large.jpg	53.9452301043047	-36.1117650864775	663752	54460.4016261574	2	[3840 3840]	[0.000416667 0.000416667]	image/jpeg	ICRS	TAN	[1920.5 1920.5]	[53.9452 -36.1118]	[-0.000416667 -0.0 -0.0 0.000416667]	UV	meters	2.35e-07	3.007e-07	1.693e-07	http://galex.stsci.edu/data/GR6/pipe/02-vsn/50423-AIS_423/d/00-visits/0002-img/07-try/qa/AIS_423_0002_sg49-xd-int_2color_large.jpg
GALEX	GALEX	ais_423_0002_sg49-xd-int_2color_medium.jpg	53.9452301043047	-36.1117650864775	575892	54460.4016261574	2	[3840 3840]	[0.000416667 0.000416667]	image/jpeg	ICRS	TAN	[1920.5 1920.5]	[53.9452 -36.1118]	[-0.000416667 -0.0 -0.0 0.000416667]	UV	meters	2.35e-07	3.007e-07	1.693e-07	http://galex.stsci.edu/data/GR6/pipe/02-vsn/50423-AIS_423/d/00-visits/0002-img/07-try/qa/AIS_423_0002_sg49-xd-int_2color_medium.jpg
GALEX	GALEX	ais_423_0002_sg49-xd-int_2color_small.jpg	53.9452301043047	-36.1117650864775	134331	54460.4016261574	2	[3840 3840]	[0.000416667 0.000416667]	image/jpeg	ICRS	TAN	[1920.5 1920.5]	[53.9452 -36.1118]	[-0.000416667 -0.0 -0.0 0.000416667]	UV	meters	2.35e-07	3.007e-07	1.693e-07	http://galex.stsci.edu/data/GR6/pipe/02-vsn/50423-AIS_423/d/00-visits/0002-img/07-try/qa/AIS_423_0002_sg49-xd-int_2color_small.jpg
GALEX	GALEX	ais_423_0002_sg49-xd-int_2color.jpg	53.9452301043047	-36.1117650864775	3487526	54460.4015972222	2	[3840 3840]	[0.000416667 0.000416667]	image/jpeg	ICRS	TAN	[1920.5 1920.5]	[53.9452 -36.1118]	[-0.000416667 -0.0 -0.0 0.000416667]	UV	meters	1.573e-07	1.806e-07	1.34e-07	http://galex.stsci.edu/data/GR6/pipe/02-vsn/50423-AIS_423/d/00-visits/0002-img/07-try/qa/AIS_423_0002_sg49-xd-int_2color.jpg
GALEX	GALEX	ais_423_0002_sg49-xd-int_2color_large.jpg	53.9452301043047	-36.1117650864775	663752	54460.4015972222	2	[3840 3840]	[0.000416667 0.000416667]	image/jpeg	ICRS	TAN	[1920.5 1920.5]	[53.9452 -36.1118]	[-0.000416667 -0.0 -0.0 0.000416667]	UV	meters	1.573e-07	1.806e-07	1.34e-07	http://galex.stsci.edu/data/GR6/pipe/02-vsn/50423-AIS_423/d/00-visits/0002-img/07-try/qa/AIS_423_0002_sg49-xd-int_2color_large.jpg
GALEX	GALEX	ais_423_0002_sg49-xd-int_2color_medium.jpg	53.9452301043047	-36.1117650864775	575892	54460.4015972222	2	[3840 3840]	[0.000416667 0.000416667]	image/jpeg	ICRS	TAN	[1920.5 1920.5]	[53.9452 -36.1118]	[-0.000416667 -0.0 -0.0 0.000416667]	UV	meters	1.573e-07	1.806e-07	1.34e-07	http://galex.stsci.edu/data/GR6/pipe/02-vsn/50423-AIS_423/d/00-visits/0002-img/07-try/qa/AIS_423_0002_sg49-xd-int_2color_medium.jpg
GALEX	GALEX	ais_423_0002_sg49-xd-int_2color_small.jpg	53.9452301043047	-36.1117650864775	134331	54460.4015972222	2	[3840 3840]	[0.000416667 0.000416667]	image/jpeg	ICRS	TAN	[1920.5 1920.5]	[53.9452 -36.1118]	[-0.000416667 -0.0 -0.0 0.000416667]	UV	meters	1.573e-07	1.806e-07	1.34e-07	http://galex.stsci.edu/data/GR6/pipe/02-vsn/50423-AIS_423/d/00-visits/0002-img/07-try/qa/AIS_423_0002_sg49-xd-int_2color_small.jpg
GALEX	GALEX	ais_423_0002_sg49-fd-int.fits.gz	53.9452301043047	-36.1117650864775	1176575	54460.4015972222	2	[3840 3840]	[0.000416667 0.000416667]	image/fits	ICRS	TAN	[1920.5 1920.5]	[53.9452 -36.1118]	[-0.000416667 -0.0 -0.0 0.000416667]	UV	meters	1.573e-07	1.806e-07	1.34e-07	http://galex.stsci.edu/data/GR6/pipe/02-vsn/50423-AIS_423/d/00-visits/0002-img/07-try/AIS_423_0002_sg49-fd-int.fits.gz
GALEX	GALEX	ais_423_0002_sg49-xd-int_2color_medium_annot.jpg	53.9452301043047	-36.1117650864775	618132	54460.4015972222	2	[3840 3840]	[0.000416667 0.000416667]	image/jpeg	ICRS	TAN	[1920.5 1920.5]	[53.9452 -36.1118]	[-0.000416667 -0.0 -0.0 0.000416667]	UV	meters	1.573e-07	1.806e-07	1.34e-07	http://galex.stsci.edu/data/GR6/pipe/02-vsn/50423-AIS_423/d/00-visits/0002-img/07-try/qa/AIS_423_0002_sg49-xd-int_2color_medium_annot.jpg

#  Let's see what HEASARC offers, and this time limit it to FITS 
#   this option doesn't currently work for STScI's service)
im_table_heasarc=galex_heasarc.search(pos=pos,size=0.1,format='image/fits')
im_table_heasarc.to_table()

Table length=2

Survey	Ra	Dec	Dim	Size	Scale	Format	PixFlags	URL	LogicalName
object	float64	float64	int32	object	object	object	object	object	object
galexnear	53.40190833	-36.14065833	2	[300 300]	[-0.0003333333333333334 0.0003333333333333334]	image/fits	F	https://skyview.gsfc.nasa.gov/cgi-bin/images?position=53.40190833%2C-36.14065833&survey=galexnear&pixels=300%2C300&sampler=LI&size=0.10000000000000002%2C0.10000000000000002&projection=Tan&coordinates=J2000.0&requestID=skv1621888259202&return=FITS	1
galexfar	53.40190833	-36.14065833	2	[300 300]	[-0.0003333333333333334 0.0003333333333333334]	image/fits	F	https://skyview.gsfc.nasa.gov/cgi-bin/images?position=53.40190833%2C-36.14065833&survey=galexfar&pixels=300%2C300&sampler=LI&size=0.10000000000000002%2C0.10000000000000002&projection=Tan&coordinates=J2000.0&requestID=skv1621888259484&return=FITS	2

## If you only run this once, you can do it in memory in one line:
##  This fetches the FITS as an astropy.io.fits object in memory
#dataobj=im_table_heasarc[0].getdataobj()
## But if you might run this notebook repeatedly with limited bandwidth, 
##  download it once and cache it.  
file_name = download_file(im_table_heasarc[0].getdataurl(),cache=True)
dataobj=fits.open(file_name)
print(type(dataobj))

<class 'astropy.io.fits.hdu.hdulist.HDUList'>

# Get the FITS file (which is index 0 for the NUV image or index=2 for the FUV image)
from pylab import figure, cm
from matplotlib.colors import LogNorm
plt.matshow(dataobj[0].data, origin='lower', cmap=cm.gray_r, norm=LogNorm(vmin=0.005, vmax=0.3))

<matplotlib.image.AxesImage at 0x7fd930db9610>

Hint: Repeat steps for X-ray image. (Note: Ideally, we would find an image in the Chandra 'cxc' catalog)

x_services=vo.regsearch(servicetype='image',keywords=['chandra'], waveband='x-ray')
print(x_services.to_table()['short_name','ivoid'])

 short_name                 ivoid                 
----------- --------------------------------------
        CDA         ivo://cxc.harvard.edu/cda.siap
        CSC         ivo://cxc.harvard.edu/csc.siap
      CSCR1       ivo://cxc.harvard.edu/cscr1.siap
      CSCR2       ivo://cxc.harvard.edu/cscr2.siap
    Chandra          ivo://nasa.heasarc/chanmaster
GOODSACISFB ivo://nasa.heasarc/skyview/goodsacisfb

## Do an image search for NGC 1365 in the X-ray CDA service found above
xim_table=x_services[0].search(pos=pos,size=0.2)
## Some of these are FITS and some JPEG.  Look at the columns:
print( xim_table.to_table().columns )
first_fits_image_row = [x for x in xim_table if 'image/fits' in x.format][0] 

<TableColumns names=('name','instrument','date_obs','ra','dec','naxis','imgscale','imgfmt','accref','filesize','obsid')>

## Create an image from the first FITS file (index=1) by downloading:
## See above for options
#xhdu_list=first_fits_image_row.getdataobj()
file_name = download_file(first_fits_image_row.getdataurl(),cache=True)
xhdu_list=fits.open(file_name)


plt.imshow(xhdu_list[0].data, origin='lower', cmap='cool', norm=LogNorm(vmin=0.1, vmax=500.))
plt.xlim(460, 560)
plt.ylim(460, 560)

(460.0, 560.0)

Step 3: Make a spectrum:

Find what Chandra spectral observations exist already for this source.

Hint: try searching for X-ray spectral data tables using the registry query

# Use the TAP protocol to list services that contain X-ray spectral data
xsp_services=vo.regsearch(servicetype='ssa',waveband='x-ray')
xsp_services.to_table()['short_name','ivoid','waveband']

Table length=4

short_name	ivoid	waveband
object	object	object
Chandra	ivo://nasa.heasarc/chanmaster	x-ray
INTEGRAL/BSC	ivo://nasa.heasarc/intbsc	gamma-ray#x-ray
NED/SED	ivo://ned.ipac/sed_data_near_position	radio#millimeter#infrared#optical#uv#euv#x-ray#gamma-ray
HEAVENS @ ISDC	ivo://wfau.roe.ac.uk/heavens_at_isdc/light-curves	x-ray#gamma-ray

Hint 2: Take a look at what data exist for our candidate, NGC 1365.

spec_tables=xsp_services[0].search(pos=pos,radius=0.2,verbose=True)
spec_tables.to_table()

Table length=2

obsid	status	name	ra	dec	time	detector	grating	exposure	type	pi	public_date	SSA_start_time	SSA_tmid	SSA_stop_time	SSA_duration	SSA_coord_obs	SSA_ra	SSA_dec	SSA_title	SSA_reference	SSA_datalength	SSA_datamodel	SSA_instrument	SSA_publisher	SSA_format	SSA_fov	SSA_wavelength_min	SSA_wavelength_max	SSA_bandwidth	SSA_bandpass
			deg	deg	mjd			s			mjd	mjd	mjd	mjd	s	deg	deg	deg								deg	meter	meter	meter	meter
object	object	object	float64	float64	float64	object	object	float64	object	object	int32	float64	float64	float64	float64	float64	float64	float64	object	object	object	object	object	object	object	float64	float64	float64	float64	float64
13921	archived	NGC 1365	53.40167	-36.14028	56029.0272	ACIS-S	HETG	110050	GO	Reeves	56398	56029.0271643518	--	--	110050.0	--	53.40167	-36.14028	acisf13921N001_pha2	https://heasarc.gsfc.nasa.gov/FTP/chandra/data/science/ao13/cat7/13921/primary/acisf13921N001_pha2.fits.gz	12	Spectrum-1.0	ACIS-S	HEASARC	application/fits	0.81	1.2398e-10	6.1992e-09	6.07522e-09	3.16159e-09
13920	archived	NGC 1365	53.40167	-36.14028	56026.4065	ACIS-S	HETG	90040	GO	Reeves	56398	56026.4065162037	--	--	90040.0	--	53.40167	-36.14028	acisf13920N002_pha2	https://heasarc.gsfc.nasa.gov/FTP/chandra/data/science/ao13/cat7/13920/primary/acisf13920N002_pha2.fits.gz	12	Spectrum-1.0	ACIS-S	HEASARC	application/fits	0.81	1.2398e-10	6.1992e-09	6.07522e-09	3.16159e-09

Hint 3: Download the data to make a spectrum. Note: you might end here and use Xspec to plot and model the spectrum. Or ... you can also try to take a quick look at the spectrum.

#  Get it and look at it:
#hdu_list=spec_tables[0].getdataobj()
file_name = download_file(spec_tables[0].getdataurl(),cache=True)
hdu_list=fits.open(file_name)

spectra=hdu_list[1].data
print(spectra.columns)
print(len(spectra))

ColDefs(
    name = 'SPEC_NUM'; format = '1I'; null = 0
    name = 'TG_M'; format = '1I'; null = 99
    name = 'TG_PART'; format = '1I'
    name = 'TG_SRCID'; format = '1I'; null = 0
    name = 'X'; format = '1E'; unit = 'pixel'
    name = 'Y'; format = '1E'; unit = 'pixel'
    name = 'CHANNEL'; format = '8192I'; null = 0
    name = 'COUNTS'; format = '8192I'; unit = 'count'; null = -1
    name = 'STAT_ERR'; format = '8192E'; unit = 'count'
    name = 'BACKGROUND_UP'; format = '8192I'; unit = 'count'; null = -1
    name = 'BACKGROUND_DOWN'; format = '8192I'; unit = 'count'; null = -1
    name = 'BIN_LO'; format = '8192D'; unit = 'angstrom'
    name = 'BIN_HI'; format = '8192D'; unit = 'angstrom'
)
12

## Or write it to disk
import os
if not os.path.isdir('downloads'):
    os.makedirs("downloads")
fname=spec_tables[0].make_dataset_filename()
#  Known issue where the suffix is incorrect:
fname=fname.replace('None','fits')
with open('downloads/{}'.format(fname),'wb') as outfile:
    outfile.write(spec_tables[0].getdataset().read())

Extension: Making a "quick look" spectrum. For our purposes, the 1st order of the HEG grating data would be sufficient.

j=1
for i in range(len(spectra)):
    matplotlib.rcParams['figure.figsize'] = (8, 3)
    if abs(spectra['TG_M'][i]) == 1 and (spectra['TG_PART'][i]) == 1:
        ax=plt.subplot(1,2,j)
        pha = plt.plot( spectra['CHANNEL'][i],spectra['COUNTS'][i])
        ax.set_yscale('log')
        if spectra['TG_PART'][i] == 1:
            instr='HEG'
        ax.set_title("{grating}{order:+d}".format(grating=instr, order=spectra['TG_M'][i]))
        plt.tight_layout()
        j=j+1

This can then be analyzed in your favorite spectral analysis tool, e.g., pyXspec. (For the winter 2018 AAS workshop, we demonstrated this in a notebook that you can consult for how to use pyXspec, but the pyXspec documentation will have more information.)

Congratulations! You have completed this notebook exercise.