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Download this as a Jupyter notebook! <notebooks/lean-tutorial.ipynb>

Lean Tutorial

This tutorial runs through all of the steps for doing a project with Marvin from start-to-finish with no extra fat. We recommend the use of ipython or jupyter notebook when using Marvin. You can start either from a terminal with ipython or jupyter notebook.

Project Description

Calculate the [NII]/Hα ratio for star-forming spaxels in galaxies with stellar mass between 1010 and 1011 .

Sample Selection

Marvin uses a simplified query syntax (in both Web and local queries) that understands the MaNGA database schema, so you don't have to write complicated SQL queries.

Goal: Find galaxies with stellar mass between 1010 and 1011.

Create the query with ~marvin.tools.query.query.doQuery and run it (limit to only 3 results for demo purposes):

>>> from marvin.tools.query import doQuery
>>> q, r = doQuery(search_filter='nsa.sersic_logmass >= 10 and nsa.sersic_logmass <= 11', limit=3)

Tip: see Marvin Query <marvin-query> to learn the basics of querying. See Example Queries <marvin-query-examples> and Marvin Query Syntax Tutorial <marvin-sqlboolean> for help with designing search filters.

View the ~marvin.tools.query.results.Results. You may see a different set of results. That is ok as long as you see some set of results.:

>>> df = r.toDF()
>>> df
    mangaid     plateifu    sersic_logmass
0   1-109056    8077-6103   10.200446
1   1-109081    8077-12705  10.862523
2   1-109112    8078-1901   10.128309

Convert into ../tools/maps objects:

>>> r.convertToTool('maps')
>>> r.objects
>>> galaxies = r.objects

Get the Maps

Alternatively, maybe we already knew our galaxy IDs, which we can use to create ~marvin.tools.maps.Maps objects:

>>> from marvin.tools.maps import Maps
>>> mangaids = ['1-245458', '1-22301', '1-605884']
>>> galaxies = [Maps(mangaid=mangaid) for mangaid in mangaids]

Get the Hα maps:

>>> haflux_maps = [galaxy['emline_gflux_ha_6564'] for galaxy in galaxies]

Plot Hα map of the second galaxy:

>>> haflux_map = haflux_maps[1]
>>> fig, ax = haflux_map.plot()

>>> from marvin.tools.maps import Maps >>> maps = Maps('1-22301') >>> haflux_map = maps.emline_gflux_ha_6564 >>> fig, ax = haflux_map.plot()

Get Spectrum and Model Fit

Let's take a look at the model fits a spaxel. The easiest way is to navigate to the Galaxy page for 7992-6101 and click on the red "Map/SpecView Off" button.

However, we can also plot the spectrum and model fits in Python. First, we can find the coordinates of a spaxel by moving our cursor around the interactive matplotlib plotting window. When the cursor is over the spaxel of interest, the coordinates will appear in the lower right.

Then we can create a ~marvin.tools.spaxel.Spaxel object by accessing the parent ~marvin.tools.maps.Maps object from the ~marvin.tools.quantities.Map object (haflux_map.maps) and retrieve the model fit.

>>> spax = galaxies[1].getSpaxel(x=28, y=24, xyorig='lower', cube=True, modelcube=True)

Now let's plot the spectrum and model fit:

>>> import matplotlib.pyplot as plt
>>> # Set matplotlib style sheet. Undo with matplotib.rcdefaults().
>>> plt.style.use('seaborn-darkgrid')

>>> ax = spax.flux.plot()
>>> ax.plot(spax.full_fit.wavelength, spax.full_fit.value)
>>> ax.legend(list(ax.get_lines()), ['observed', 'model'])
>>> ax.axis([7100, 7500, 0.3, 0.65])

>>> import matplotlib.pyplot as plt >>> plt.style.use('seaborn-darkgrid') >>> from marvin.tools.maps import Maps >>> maps = Maps('1-22301') >>> spax = maps.getSpaxel(x=28, y=24, xyorig='lower', cube=True, modelcube=True) >>> ax = spax.flux.plot() >>> ax.plot(spax.full_fit.wavelength, spax.full_fit.value) >>> ax.legend(list(ax.get_lines()), ['observed', 'model']) >>> ax.axis([7100, 7500, 0.3, 0.65])

Plot BPT Diagram

The ~marvin.tools.maps.Maps.get_bpt returns masks for spaxels of different ionization types and the Figure object.

>>> masks, fig, axes = galaxies[1].get_bpt()

>>> from marvin.tools.maps import Maps >>> maps = Maps('1-22301') >>> masks, fig, axes = maps.get_bpt()

For a detailed description see ../tools/bpt.

Select Star-forming Spaxels

Select the star-forming spaxels that are in the star-forming region of each diagnostic diagram (hence the "global" keyword):

>>> sf = masks['sf']['global']

Return the complement of the BPT global star-forming mask (True means star-forming) using ~ and mark those spaxels as DONOTUSE since they are non-star-forming spaxels.

>>> mask_non_sf = ~sf * haflux_map.pixmask.labels_to_value('DONOTUSE')

Do a bitwise OR between the DAP mask and the non-star-forming mask:

>>> mask = haflux_map.mask | mask_non_sf

Plot with our new mask:

>>> haflux_map.plot(mask=mask)

>>> from marvin.tools.maps import Maps >>> maps = Maps('1-22301') >>> haflux_map = maps.emline_gflux_ha_6564 >>> masks = maps.get_bpt(show_plot=False, return_figure=False) >>> sf = masks['sf']['global'] >>> mask_non_sf = ~sf * haflux_map.pixmask.labels_to_value('DONOTUSE') >>> mask = haflux_map.mask | mask_non_sf >>> haflux_map.plot(mask=mask)

Plot [NII]/Hα Flux Ratio for Star-forming Spaxels

Calculate [NII]6585/Hα flux ratio:

>>> maps_7992_6101 = galaxies[1]
>>> nii = maps_7992_6101['emline_gflux_nii_6585']
>>> ha = maps_7992_6101['emline_gflux_ha_6564']
>>> nii_ha = nii / ha

Plot the [NII]/Hα flux ratio for the star-forming spaxels:

>>> nii_ha.plot(mask=mask, cblabel='[NII]6585 / Halpha flux ratio')

>>> from marvin.tools.maps import Maps >>> maps = Maps('1-22301') >>> nii = maps['emline_gflux_nii_6585'] >>> ha = maps['emline_gflux_ha_6564'] >>> masks = maps.get_bpt(show_plot=False, return_figure=False) >>> sf = masks['sf']['global'] >>> mask_non_sf = ~sf * ha.pixmask.labels_to_value('DONOTUSE') >>> mask = ha.mask | mask_non_sf >>> nii_ha = nii / ha >>> nii_ha.plot(mask=mask, cblabel='[NII]6585 / Halpha flux ratio')

Next Steps

  • ../getting-started (more general introduction to Marvin)
  • plotting-tutorial
  • Download Data <../core/downloads> (avoid repeating the same remote API calls every time you run your script)