First Steps

Now that you have installed Marvin, it's time to take your first steps. If you want to learn more about how Marvin works, then go see General Info to learn about Marvin Modes, Versions, or Downloading. If you just want to play, then read on.

First let's run some boilerplate code for Python 2/3 compatibility and plotting in the notebook:



In [1]:

    
from __future__ import print_function, division, absolute_import
import matplotlib.pyplot as plt
%matplotlib inline

Now, let’s import Marvin:



In [2]:

    
import marvin









    



INFO: No release version set. Setting default to MPL-5

Let's see what release we're using. Releases can be either MPLs (e.g. MPL-5) or DRs (e.g. DR13), however DRs are currently disabled in Marvin.



In [3]:

    
marvin.config.release









    Out[3]:





'MPL-5'

On intial import, Marvin will set the default data release to use the latest MPL available, currently MPL-5. You can change the version of MaNGA data using the Marvin Config.



In [7]:

    
from marvin import config
config.setRelease('MPL-3')

print('MPL:', config.release)









    



MPL: MPL-3

You can also specifically change releases using the setMPL method as well:



In [10]:

    
config.setMPL('MPL-5')
print('MPL:', config.release)









    



MPL: MPL-5

But let's work with MPL-4:



In [11]:

    
config.setRelease('MPL-4')

# check designated version
config.release









    Out[11]:





'MPL-4'

My First Cube

Now let’s play with a Marvin Cube!

Import the Marvin-Tools Cube class:



In [12]:

    
from marvin.tools.cube import Cube

Let's load a cube from a local file. Start by specifying the full path and name of the file, such as:

/Users/Brian/Work/Manga/redux/v1_5_1/8485/stack/manga-8485-1901-LOGCUBE.fits.gz

EDIT Next Cell



In [13]:

    
#----- EDIT THIS CELL -----#

# filename = '/Users/Brian/Work/Manga/redux/v1_5_1/8485/stack/manga-8485-1901-LOGCUBE.fits.gz'
filename = 'path/to/manga/cube/manga-8485-1901-LOGCUBE.fits.gz'

filename = '/Users/andrews/manga/spectro/redux/v1_5_1/8485/stack/manga-8485-1901-LOGCUBE.fits.gz'
filename = '/Users/Brian/Work/Manga/redux/v1_5_1/8485/stack/manga-8485-1901-LOGCUBE.fits.gz'

Create a Cube object:



In [14]:

    
cc = Cube(filename=filename)









    



WARNING: FITSFixedWarning: PLATEID = 8485 / Current plate 
a string value was expected. [astropy.wcs.wcs]

Now we have a Cube object:



In [15]:

    
print(cc)









    



<Marvin Cube (plateifu='8485-1901', mode='local', data_origin='file')>

How about we look at some meta-data



In [16]:

    
cc.ra, cc.dec, cc.header['SRVYMODE']









    Out[16]:





(232.544703894, 48.6902009334, 'MaNGA dither')

...and the quality and target bits



In [17]:

    
cc.targetbit









    Out[17]:





{'bits': [2336L], 'names': ['MNGTRG1']}



In [18]:

    
cc.qualitybit









    Out[18]:





('DRP3QUAL', 1L, None)

Get a Spaxel

Cubes have several functions currently available: getSpaxel, getMaps, getAperture. Let's look at spaxels. We can retrieve spaxels from a cube easily via indexing. In this manner, spaxels are 0-indexed from the lower left corner. Let's get spaxel (x=10, y=10):



In [34]:

    
spax = cc[10,10]



In [36]:

    
# print the spaxel to see the x,y coord from the lower left, and the coords relative to the cube center, x_cen/y_cen
spax









    Out[36]:





<Marvin Spaxel (x=10, y=10; x_cen=-7, y_cen=-7>

Spaxels have a spectrum associated with it. It has the wavelengths and fluxes of each spectral channel:

Alternatively grab a spaxel with getSpaxel. Use the xyorig keyword to set the coordinate origin point: 'lower' or 'center'. The default is "center"



In [41]:

    
# let's grab the central spaxel
spax = cc.getSpaxel(x=0, y=0)
spax









    Out[41]:





<Marvin Spaxel (x=17, y=17; x_cen=0, y_cen=0>



In [42]:

    
spax.spectrum.wavelength









    Out[42]:





array([  3621.59598486,   3622.42998417,   3623.26417553, ...,
        10349.03843826,  10351.42166679,  10353.80544415])



In [43]:

    
spax.spectrum.flux









    Out[43]:





array([ 0.4740223 ,  0.41714188,  0.47762075, ...,  0.        ,
        0.        ,  0.        ], dtype=float32)

Plot the spectrum!



In [44]:

    
# turn on interactive plotting
%matplotlib notebook



In [45]:

    
spax.spectrum.plot()









    














    











    Out[45]:





<matplotlib.axes._subplots.AxesSubplot at 0x11682d7d0>

Save plot to Downloads directory:



In [30]:

    
# To save the plot, we need to draw it in the same cell as the save command.
spax.spectrum.plot()

import os
plt.savefig(os.getenv('HOME') + '/Downloads/my-first-spectrum.png')

# NOTE - if you are using the latest version of iPython and Jupyter notebooks, then interactive matplotlib plots 
# should be enabled.  You can save the figure with the save icon in the interactive toolbar.



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