Instrument Tools


Add description of Spectrum object class.


Downloading and reading APOGEE data files

To download APOGEE spectrum by 2MASS name and data type aspcap, or apstar:

import apogee_tools as ap'2M03425325+2326495', type='aspcap')'2M03425325+2326495', type='apstar')

For data type apvisit or ap1d:'2M03425325+2326495', type='apvisit')'2M03425325+2326495', type='ap1d', visit=1, frame=1)

Note: type='apvisit' will download the spectra for all visits observed, while type='ap1d' will download only the visit specified (and if not specified, will default to visit=1, frame=1).

For information on APOGEE data files, see the following:

  • aspcap - combined, continuum normalized spectra
  • apStar - combined spectra
  • apVisit - individual raw visit spectra with telluric correction
  • ap1D - individual raw visit spectra with NO telluric correction

Also for info about the allStar file (such as aspcap pipeline parameters and photometry for all of the sources), see: allStar.

Once the data for a source has been downloaded, read aspcap or apStar files by specifying the 2MASS name and data type:

data = ap.Apogee(id='2M03425325+2326495', type='aspcap')

Or for single visit spectrum, indicate the index of the visit number at the end:

data = ap.Apogee(id='2M03425325+2326495', type='apvisit', visit=1)

Search the APOGEE catalog

Example search–will search the allStar-l30e.2.fits you downloaded:

params = ['TEFF', 'LOGG', 'M_H']
ranges = [[-10000,4000], [0,5], [-2,2]]
source_table = ap.multiParamSearch(par=params, select=ranges, dir='/path_to/')

Look up aspcap parameters in allStar-l30e.2.fits for specific list of 2MASS IDs:

tm_ids = ['2M01195227+8409327']
ap_dict = ap.returnAspcapTable(tm_ids, params=['TEFF', 'LOGG', 'M_H', 'SNR'], save=False)

Plot data

Some plotting examples:

data = ap.Apogee(id='2M03290406+3117075', type='aspcap')

# plot spectrum

# plot aspcap model and noise:
data.plot(items=['spec', 'model', 'noise'], save=True)

# plot indentified lines (from Souto 2016):
data.plot(items=['spec', 'lines'], xrange=[15200,15500], yrange=[.6,1.2])

Mask outlying flux

Specify number of standard deviations above and below the mean of the flux to cut (sigma = [lower cuttoff, upper cutoff]), and the number pixels to buffer each side of the cut (pixel_buffer = [lower mask pixel buffer, upper mask pixel buffer]):

data.mask(sigma=[3,2], pixel_buffer=[0,3])

Chi-squared comparison

Compare two spectra; return chi (chi-squared value between data and mdl), norm_data (data spectrum normalized), and scaled_mdl (mdl which has been scaled to data):

chi, norm_data, scaled_mdl = ap.compareSpectra(data, mdl)



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