DAPall File Construction¶
Analysis class: mangadap.survey.dapall.DAPall
File Root: $MANGA_SPECTRO_ANALYSIS/$MANGADRP_VER/$MANGADAP_VER
File Template: dapall-$MANGADRP_VER-$MANGADAP_VER.fits
- Important class dependencies:
mangadap.survey.drpcomplete.DRPComplete
: Provides the database used to construct whichPLATEIFU
observations were analyzed.mangadap.par.analysisplan.AnalysisPlan
: Identifies the unique analysis approaches used to analyze each datacube.mangadap.dapfits.DAPMapsBitMask
: Interprets the masks in theMAPS
files.- Many of the other core classes are needed but only to define the methods used by the analysis approaches selected.
- Algorithm:
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 headerCalculate 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
mangadap.survey.dapall.DAPall._radial_coverage_metric()
.Pull the S/N metrics from the
MAPS
headerGet the mean g-band surface brightness within 1 \(R_e\).
Get the binning metrics using
mangadap.survey.dapall.DAPall._binning_metrics()
.Get the stellar kinematics metrics using
mangadap.survey.dapall.DAPall._stellar_kinematics_metrics()
.Get the H-alpha kinematics metrics using
mangadap.survey.dapall.DAPall._halpha_kinematics_metrics()
.Get the emission-line metrics using
mangadap.survey.dapall.DAPall._emission_line_metrics()
.Get the spectral-index metrics using
mangadap.survey.dapall.DAPall._spectral_index_metrics()
.Calculate the star-formation rates based on the 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