DAPall File Construction

Analysis class: DAPall


File Template: dapall-$MANGADRP_VER-$MANGADAP_VER.fits

Important class dependencies:

  • DRPComplete: Provides the database used to construct which PLATEIFU observations were analyzed.

  • AnalysisPlan: Identifies the unique analysis approaches used to analyze each datacube.

  • DAPMapsBitMask: Interprets the masks in the MAPS files.

  • Many of the other core classes are needed but only to define the methods used by the analysis approaches selected.


  • Instantiate the class used to perform cosmology calculations, astropy.cosmology.FlatLambdaCDM, where we set \(h=1\), \(\Omega_M = 0.3\), and \(\Omega_\Lambda = 0.7\).

  • Parse the plan file

  • Ensure that all plans to add compute the same emission-line moments, emission-line models, and spectral indices.

  • Create list of possibly complete observations and analysis products.

  • For each MAPS file that should exist:

    • Find the associated row in the DRPall file, and copy some of those data.

    • Calculate the luminosity and angular diameter distance based on the NSA redshift

    • Check that the file exists, and if so continue

    • Grab information from the MAPS file header

    • Calculate the luminosity and angular diameter distance based on the input guess redshift (usually the same as the NSA redshift)

    • Calculate the radial coverage metric using _radial_coverage_metric().

    • Pull the S/N metrics from the MAPS header

    • Get the mean g-band surface brightness within 1 \(R_e\).

    • Get the binning metrics using _binning_metrics().

    • Get the stellar kinematics metrics using _stellar_kinematics_metrics().

    • Get the \({\rm H}\alpha\) kinematics metrics using _halpha_kinematics_metrics().

    • Get the emission-line metrics using _emission_line_metrics().

    • Get the spectral-index metrics using _spectral_index_metrics().

    • Calculate the star-formation rates based on the \({\rm H}\alpha\) flux within 1 \(R_e\) and over the full FOV. E.g.,

      log_Mpc_in_cm = numpy.log10(astropy.constants.pc.to('cm').value) + 6
      log_halpha_luminosity_1re = numpy.log10(4*numpy.pi) \
                  + numpy.log10(db['EMLINE_GFLUX_1RE'][i,self.elfit_channels['Ha-6564']]) \
                  - 17 + 2*numpy.log10(db['LDIST_Z'][i]) + 2*log_Mpc_in_cm
      db['SFR_1RE'][i] = numpy.power(10, log_halpha_luminosity_1re - 41.27)
  • Add the channel names to the header