Feature Overview

Here we describe the broad features to give a sense of the types of analyses which can be performed and the general functionality of the code.

Generalized Plots

Several types of basic plots can be made, particularly for cosmological simulations. These routines are quite general and can plot any quantity at the particle or catalog level which is known.

For example, for general particle-level plots:

sim = temet.sim(res=1820, run='tng', redshift=0.0)
sim2 = temet.sim(res=910, run='tng', redshift=0.0)

plot.general.plotHistogram1D([sim], 'gas', 'temp')
plot.general.plotHistogram1D([sim], 'gas', 'temp', subhaloIDs=[0,1,2,3,4])
plot.general.plotHistogram1D([sim, sim2], 'gas', 'dens', qRestrictions=[('temp',5.0,np.inf)])

plot.general.plotPhaseSpace2D(sim, 'gas', xQuant='numdens', yQuant='temp')
plot.general.plotPhaseSpace2D(sim, 'gas', xQuant='numdens', yQuant='temp', meancolors=['dens','O VI frac'])

plot.general.plotParticleMedianVsSecondQuant(sim, 'dm', xQuant='velmag', yQuant='veldisp')

plot.general.plotStackedRadialProfiles1D([sim], subhaloIDs=[0], 'gas', ptProperty='entropy', op='median')
plot.general.plotStackedRadialProfiles1D([sim,sim2], subhaloIDs=[[0,1,2],[1,4,5]], 'gas', ptProperty='bmag')

And for general group catalog-level plots:

sim = temet.sim(run='tng300-1', redshift=0.0)

plot.cosmoGeneral.quantHisto2D(sim, pdf=None, yQuant='sfr2_surfdens', xQuant='mstar2_log', cenSatSelect='cen')
plot.cosmoGeneral.quantHisto2D(sim, pdf=None, yQuant='stellarage', xQuant='mstar_30pkpc', cQuant='Krot_stars2')

plot.cosmoGeneral.quantSlice1D([sim], pdf=None, xQuant='sfr2', yQuants=['BH_BolLum','Z_gas'], sQuant='mstar_30pkpc_log', sRange=[10.0,10.2])

plot.cosmoGeneral.quantMedianVsSecondQuant([sim], pdf=None, yQuants=['Z_stars','ssfr'], xQuant='mhalo_200')

Note that at the bottom of plot/general.py and plot/cosmoGeneral.py there are several “driver” functions which show more complex examples of making these types of plots, including advanced functionality, and automatic generation of large sets of plots exploring all possible relationships and quantities. The configuration and metadata of known simulation properties can be found in plot/quantities.py.

Visualization

There are broadly two types of visualizations: box-based and halo-based, spanning all particle types and fields.

  • vis.boxDrivers contains numerous driver functions which create different types of full box images.

  • vis.boxMovieDrivers create frames for movies, including many of the available TNG movies.

  • vis.haloDrivers as above, except targeted for images of individual galaxies and/or halos.

  • vis.haloMovieDrivers generate frames for halo/galaxy-centric movies, including time/merger tree tracking.

All such driver functions generally set a large number of configurable options, which are then passed into the extremely general vis.box.renderBox() or vis.halo.renderSingleHalo() functions, which accept a large number of arguments (see code for details).

One could in theory call these functions directly:

panels = []
panels.append( {'partType':'dm', 'partField':'coldens_msunkpc2'} )
panels.append( {'partType':'gas', 'partField':'coldens_msunkpc2'} )

commonVars = {'run':'tng', 'res':270, 'redshift':0.0}

class plotConfig:
    pass

vis.box.renderBox(panels, plotConfig, commonVars)

each entry in panels will add one image/view/panel to the figure, and these can differ in any way possible by setting different parameters. The plotConfig class holds settings which affect the overall figure as a whole, and commonVars (which is usually all local variables in the driver functions) are shared between all panels.

For a halo-based render, the process is the same, and subhaloInd specifies the subhalo ID:

run = 'tng50-3'
redshift = 0.0

sim = temet.sim(run=run, redshift=redshift)
fof10 = sim.halo(10)
fof11 = sim.halo(11)

panels = []
panels.append( {'subhaloInd':fof10['GroupFirstSub'], 'partType':'gas', 'partField':'coldens_msunkpc2'} )
panels.append( {'subhaloInd':fof10['GroupFirstSub'], 'partType':'gas', 'partField':'temp'} )
panels.append( {'subhaloInd':fof11['GroupFirstSub'], 'partType':'gas', 'partField':'coldens_msunkpc2'} )
panels.append( {'subhaloInd':fof11['GroupFirstSub'], 'partType':'gas', 'partField':'temp'} )

class plotConfig:
    plotStyle = 'edged'

vis.halo.renderSingleHalo(panels, plotConfig, locals())

Data Catalogs

All “supplementary data catalog” types products are produced in cosmo/auxcatalog.py. At the bottom of this file all known data catalog names are listed, together with their functional definition (i.e., how to compute them, and with what parameters). For example,

'Subhalo_StellarZ_SDSSFiber_rBandLumWt'    : \
     partial(subhaloRadialReduction,ptType='stars',ptProperty='metal',op='mean',rad='sdss_fiber',weighting='bandLum-sdss_r'),

gives the generating function of this catalog. A large number of catalogs are produced with the subhaloRadialReduction(), subhaloStellarPhot(), and subhaloRadialProfile() functions, which are fully generalized to operate on any particle type and field with different statistical reductions, aperture definitions, weighting, particle restrictions, and so on.

These functions implement the pSplit parallelization scheme, meaning that all such computations can be chunked and partial subsets of the full group catalog can be operated on at once, to reduce memory usage and distribute computational cost. For example:

sim = temet.sim(run='tng100-1', redshift=2.0)
for i in range(8):
    x = sim.auxCat('Subhalo_Mass_30pkpc_Stars', pSplit=[i,8])

In general, loading an auxCat which has already been created:

x = sim.auxCat('Subhalo_Mass_30pkpc_Stars')

Synthetic Observations

Functionality for creating “mock” or “synthetic” observations of certain types is included for:

  • Gas absorption (i.e. column densities) for hydrogen and metal ions (using CLOUDY postprocessing).

  • Gas absorption spectra (and equivalent widths) for hydrogen and metal ions, using the Voronoi mesh.

  • Gas emission (i.e. line fluxes) using CLOUDY postprocessing.

  • X-ray emission using APEC/AtomDB models.

  • Stellar light emission, optionally including dust attenuation models and/or emission lines, using FSPS.

  • Stellar light and dust emission, using SKIRT radiative transfer postprocessing.

Known Simulation Families

A few suites of simulations (those available on the MPCDF storage systems) are ‘known’ and can be selected by name. In this case metadata and other important attributes are hardcoded in simParams. Currently, the simulation families specified in this way are:

  • Illustris

    • Illustris-1, Illustris-2, Illustris-3, Illustris-1-Dark, Illustris-2-Dark, Illustris-3-Dark, Illustris-2-NR, Illustris-3-NR

  • TNG100

    • TNG100-1, TNG100-2, TNG100-3, TNG100-1-Dark, TNG100-2-Dark, TNG100-3-Dark

  • TNG300

    • TNG300-1, TNG300-2, TNG300-3, TNG300-1-Dark, TNG300-2-Dark, TNG300-3-Dark

  • TNG50

    • TNG50-1, TNG50-2, TNG50-3, TNG50-4, TNG50-1-Dark, TNG50-2-Dark, TNG50-3-Dark, TNG50-4-Dark

  • TNG variations * L25n512_{xxxx}, L25n256_{xxxx}, L25n128_{xxxx} where variant={xxxx} gives the 4-digit variation number.

  • EAGLE

    • Eagle100, Eagle100-Dark (rewritten versions)

  • SIMBA

    • Simba100, Simba50 (rewritten versions)

  • Millennium

    • Millennium-1, Millennium-2 (rewritten versions)

  • TNG-Cluster

    • TNG-Cluster, TNG-Cluster-Dark

  • Auriga

    • Au{h}_L{r}, Au{h}_L{r}_DM where hInd={h} gives the halo ID, and res={r} gives the resolution level.

  • TNG zooms

    • TNG50_zoom, TNG100_zoom, TNG_zoom where hInd gives the haloID, and res gives the resolution.