Now that you have installed Marvin, it’s time to take your first steps.
Side Note: If you are new to Python, we recommend that you check out the AstroBetter Python page and Practical Python for Astronomers. You can also find many general Python tutorials on Google or try typing any specific question you have into Google. If Google returns a link to a similar question asked on Stack Overflow, then definitely start there.
At any time, you can learn more about Marvin configuration settings, Marvin data access modes, and downloading Marvin data, but if you just want to play, then read on.
From your terminal, type ipython --matplotlib. Ipython is an Interactive Python shell terminal, and the --matplotlib option enables matplotlib support, such as interactive plotting. It is recommended to always use ipython instead of python.:
> ipython --matplotlib
Let’s import Marvin
import marvin
INFO: No release version set. Setting default to MPL-6
marvin.config.release
MPL-6
On intial import, Marvin will set the default data version to use the latest MPL available. You can change the version of MaNGA data using the Marvin Config.
from marvin import config
config.setRelease('MPL-5')
config.release
MPL-5
Now let’s play with a Marvin Cube
# get a cube
from marvin.tools.cube import Cube
cc = Cube('8485-1901')
# we now have a cube object
print(cc)
<Marvin Cube (plateifu='8485-1901', mode='local', data_origin='file')>
# look at some meta-data
cc.ra, cc.dec, cc.header['SRVYMODE']
(232.544703894, 48.6902009334, 'MaNGA dither')
# look at the quality and target bits
cc.target_flags
[<Maskbit 'MANGA_TARGET1' ['SECONDARY_v1_1_0', 'SECONDARY_COM2', 'SECONDARY_v1_2_0']>,
<Maskbit 'MANGA_TARGET2' []>,
<Maskbit 'MANGA_TARGET3' []>]
cc.quality_flag
<Maskbit 'MANGA_DRP3QUAL' []>
# get a Spaxel and show its wavelength and flux arrays
spax = cc[10, 10]
spax
<Marvin Spaxel (x=10, y=10)>
spax.flux.wavelength
[3621.596, 3622.43, 3623.2642, …,10349.038, 10351.422, 10353.805]A˚[3621.596, 3622.43, 3623.2642, …,10349.038, 10351.422, 10353.805]A˚
spax.flux
[0.54676276, 0.46566465, 0.4622981, …,0, 0, 0]1×10−17ergA˚sspaxelcm2[0.54676276, 0.46566465, 0.4622981, …,0, 0, 0]1×10−17ergA˚sspaxelcm2
# plot the spectrum (you may need matplotlib.pyplot.ion() for interactive display)
spax.flux.plot()
# save plot to Downloads directory
import os
import matplotlib.pyplot as plt
plt.savefig(os.path.join(os.path.expanduser('~'), 'Downloads', 'my-first-spectrum.png'))
See the Marvin Tools (marvin.tools) section for more details and examples. And the Marvin Tools Reference for the detailed Reference Guide.
Did you read about configuring Marvin, Marvin data access modes, and downloading objects yet? Do that now!
# get a Maps object
from marvin.tools.maps import Maps
maps = Maps(mangaid='1-209232')
print(maps)
<Marvin Maps (plateifu='8485-1901', mode='local', data_origin='db', bintype=SPX, template_kin=GAU-MILESHC)>
# get the NASA-Sloan Atlas info about the galaxy
maps.nsa
# list the available map categories and channels (similar to the extensions in a DAP FITS file)
maps.datamodel
# get a map using the getMap() method...
haflux = maps.getMap('emline_gflux', channel='ha_6564')
# ...or with a shortcut
haflux2 = maps['emline_gflux_ha_6564']
# or
haflux = maps.emline_glflux_ha_6564
# If a map category has channels, then specify an individual map by joining the category name
# (e.g., 'emline_gflux') and channel name (e.g., 'ha_6564') with an underscore
# (e.g., 'emline_gflux_ha_6564'). Otherwise, just use the category name (e.g., 'stellar_vel').
# get the map values, inverse variances, and masks
haflux.value
array([[ 0., 0., 0., ..., 0., 0., 0.],
[ 0., 0., 0., ..., 0., 0., 0.],
[ 0., 0., 0., ..., 0., 0., 0.],
...,
[ 0., 0., 0., ..., 0., 0., 0.],
[ 0., 0., 0., ..., 0., 0., 0.],
[ 0., 0., 0., ..., 0., 0., 0.]])
haflux.ivar
array([[ 0., 0., 0., ..., 0., 0., 0.],
[ 0., 0., 0., ..., 0., 0., 0.],
[ 0., 0., 0., ..., 0., 0., 0.],
...,
[ 0., 0., 0., ..., 0., 0., 0.],
[ 0., 0., 0., ..., 0., 0., 0.],
[ 0., 0., 0., ..., 0., 0., 0.]])
haflux.mask
array([[1073741843, 1073741843, 1073741843, ..., 1073741843, 1073741843, 1073741843],
[1073741843, 1073741843, 1073741843, ..., 1073741843, 1073741843, 1073741843],
[1073741843, 1073741843, 1073741843, ..., 1073741843, 1073741843, 1073741843],
...,
[1073741843, 1073741843, 1073741843, ..., 1073741843, 1073741843, 1073741843],
[1073741843, 1073741843, 1073741843, ..., 1073741843, 1073741843, 1073741843],
[1073741843, 1073741843, 1073741843, ..., 1073741843, 1073741843, 1073741843]])
# use map arithmetic ( + , - , * , / , or ** )
niiflux = maps['emline_gflux_nii_6585']
nii_ha = niiflux / haflux
# plot the map
fig, ax = haflux.plot()
# save plot to Downloads directory
import os
fig.savefig(os.path.join(os.path.expanduser('~'), 'Downloads', 'my-first-map.png'))
# get the central spaxel with getSpaxel()...
spax = maps.getSpaxel(x=0, y=0)
# ...or with a shortcut (defaults to xyorig=lower, whereas getSpaxel() defaults to xyorig='center')
spax2 = maps[17, 17]
# show the DAP properties
spax.properties
For more info about maps and the DAP products, check out the DAP Getting Started pages for MPL-4 and MPL-5.
Now let’s play with a Marvin Query
# import a Marvin query convenience tool
from marvin.tools.query import doQuery
# Do a Query: select all galaxies with NSA redshift < 0.2 and only 19-fiber IFUs
q, r = doQuery(searchfilter='nsa.z < 0.2 and ifu.name=19*')
init condition [['nsa.z', '<', '0.2']]
init condition [['ifu.name', '=', '19*']]
Your parsed filter is:
and_(nsa.z<0.2, ifu.name=19*)
# How many objects met the search criteria?
r.totalcount
151
# How long did my query take?
r.query_time
datetime.timedelta(0, 0, 204274) # a Python datetime timedelta object (days, seconds, microseconds)
# see total seconds
r.query_time.total_seconds()
0.204274
# Results are returned in chunks of 10 by default
r.results
<ResultSet(set=1/129, index=0:10, count_in_set=10, total=1282)>
[ResultRow(mangaid=u'1-109394', plate=8082, plateifu=u'8082-9102', ifu_name=u'9102', z=0.0361073),
ResultRow(mangaid=u'1-113208', plate=8618, plateifu=u'8618-3701', ifu_name=u'3701', z=0.0699044),
ResultRow(mangaid=u'1-113219', plate=7815, plateifu=u'7815-9102', ifu_name=u'9102', z=0.0408897),
ResultRow(mangaid=u'1-113375', plate=7815, plateifu=u'7815-9101', ifu_name=u'9101', z=0.028215),
ResultRow(mangaid=u'1-113379', plate=7815, plateifu=u'7815-6101', ifu_name=u'6101', z=0.0171611),
ResultRow(mangaid=u'1-113403', plate=7815, plateifu=u'7815-12703', ifu_name=u'12703', z=0.0715126),
ResultRow(mangaid=u'1-113418', plate=7815, plateifu=u'7815-12704', ifu_name=u'12704', z=0.0430806),
ResultRow(mangaid=u'1-113469', plate=7815, plateifu=u'7815-12702', ifu_name=u'12702', z=0.0394617),
ResultRow(mangaid=u'1-113520', plate=7815, plateifu=u'7815-1901', ifu_name=u'1901', z=0.0167652),
ResultRow(mangaid=u'1-113525', plate=8618, plateifu=u'8618-6103', ifu_name=u'6103', z=0.0169457)]
# NamedTuples can be accessed using dotted syntax (for unique column names) or like normal tuples
r.results[0].mangaid
u'1-22438'
# see the column names
r.columns
<ParameterGroup name=Columns, n_parameters=5>
[<QueryParameter full=cube.mangaid, name=mangaid, short=mangaid, remote=mangaid, display=Manga-ID>,
<QueryParameter full=cube.plate, name=plate, short=plate, remote=plate, display=Plate>,
<QueryParameter full=cube.plateifu, name=plateifu, short=plateifu, remote=plateifu, display=Plate-IFU>,
<QueryParameter full=ifu.name, name=ifu_name, short=ifu_name, remote=ifu_name, display=Name>,
<QueryParameter full=nsa.z, name=z, short=z, remote=z, display=Redshift>]
# get a list of the full column names
r.columns.full
['cube.mangaid', 'cube.plate', 'cube.plateifu', ifu.name', 'nsa.z']
See the Marvin Query section for more details and examples. And the Marvin Query Reference for the detailed Reference Guide.
No really, go read about configuring Marvin, Marvin data access modes, and downloading objects.