In [1]:
import matplotlib
%matplotlib inline
# only necessary if you have a local DB
from marvin import config
config.forceDbOff()
http://www.sdss.org/dr13/manga/manga-target-selection/nsa/
In [7]:
from marvin.tools.cube import Cube
plateifu = '8485-1901'
cube = Cube(plateifu=plateifu)
print(cube)
In [5]:
list(cube.nsa.keys())
Out[5]:
In [6]:
# get the mass of the galaxy
cube.nsa.elpetro_logmass
Out[6]:
ivar = 0
In [8]:
from marvin.tools.maps import Maps
maps = Maps(plateifu=plateifu)
print(maps)
In [9]:
haflux = maps['emline_gflux_ha_6564']
print(haflux)
In [10]:
fig, ax = haflux.plot()
In [13]:
stvel = maps['stellar_vel']
In [14]:
fig, ax = stvel.plot()
In [15]:
stsig = maps['stellar_sigma']
In [16]:
fig, ax = stsig.plot()
Classify spaxels in a given Maps object according to BPT diagrams! Will return spaxel classifications for star-forming, composite, seyfert, liner, and ambiguous. Note: there is currently a bug in the BPT code that returns incorrect composite spaxels. This is fixed in a 2.1.2 patch that will be released soon.
In [18]:
masks, fig, ax = maps.get_bpt()
In [19]:
# this is the global mask for star-forming spaxels. It can be used to do selections on any other map property.
masks['sf']['global']
Out[19]:
In [20]:
# let's look at the h-alpha flux values for the star-forming spaxels
haflux.value[masks['sf']['global']]
Out[20]:
In [21]:
# let's get the stellar velocity values for the star-forming spaxels
stvel.value[masks['sf']['global']]
Out[21]:
In [24]:
# the BPT uses a strict classification scheme based on the BPTs for NII, SII, and OI. If you do not want to use OI,
# you can turn it off
mask, fig, ax = maps.get_bpt(use_oi=False)
the BPT uses a default minimum SNR threshold cutoff of 3 on each emission line. You can change this globally using the snr keyword. Note: this keyword will change to snrmin in the upcoming 2.12 patch.
In [25]:
masks, fig, ax = maps.get_bpt(snr_min=5)
or you change it for individual emission lines. It will use the default value of 3 for all lines you do not specify.
In [26]:
masks, fig, ax = maps.get_bpt(snr_min={'ha':5, 'sii':1})
In [ ]: