DAPall File Construction¶
Analysis class: DAPall
File Root: $MANGA_SPECTRO_ANALYSIS/$MANGADRP_VER/$MANGADAP_VER
File Template: dapall-$MANGADRP_VER-$MANGADAP_VER.fits
Important class dependencies:
DRPComplete
: Provides the database used to construct whichPLATEIFU
observations were analyzed.
AnalysisPlan
: Identifies the unique analysis approaches used to analyze each datacube.
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
_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
_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