sage_analysis.example_calcs

Here we show a myriad of functions that can be used to calculate properties from the SAGE output. By setting the correct plot toggles and calling generate_func_dict(), a dictionary containing these functions can be generated and passed to calc_properties_all_files() to calculate the properties.

The properties are stored (and updated) in the properties attribute.

We refer to ../user/analysing_sage for more information on how the calculations are handled.

Author: Jacob Seiler

sage_analysis.example_calcs.calc_BMF(model, gals, snapshot: int)

Calculates the baryon mass function of the given galaxies. That is, the number of galaxies at a given baryon (stellar + cold gas) mass.

The Model.properties["snapshot_<snapshot>"]["BMF"] array will be updated.

sage_analysis.example_calcs.calc_BTF(model, gals, snapshot: int)

Calculates the baryonic Tully-Fisher relation for spiral galaxies in the given set of galaxies.

The number of galaxies added to Model.properties["snapshot_<snapshot>"]["BTF_mass"] and Model.properties["snapshot_<snapshot>"]["BTF_vel"] arrays is given by sample_size weighted by number_spirals_passed / _num_gals_all_files. If this value is greater than number_spirals_passed, then all spiral galaxies will be used.

sage_analysis.example_calcs.calc_GMF(model, gals, snapshot: int)

Calculates the gas mass function of the given galaxies. That is, the number of galaxies at a given cold gas mass.

The Model.properties["snapshot_<snapshot>"]["GMF"] array will be updated.

sage_analysis.example_calcs.calc_SFRD_history(model, gals, snapshot: int)

Calculates the sum of the star formation across all galaxies. This will be normalized by the simulation volume to determine the density. See plot_SFRD() for full implementation.

The Model.properties["snapshot_<snapshot>"]["SFRD"] value is updated.

sage_analysis.example_calcs.calc_SMD_history(model, gals, snapshot: int)

Calculates the sum of the stellar mass across all galaxies. This will be normalized by the simulation volume to determine the density. See plot_SMD() for full implementation.

The Model.properties["snapshot_<snapshot>"]["SMD"] value is updated.

sage_analysis.example_calcs.calc_SMF(model: sage_analysis.model.Model, gals, snapshot: int, calc_sub_populations: bool = False, smf_property_name: str = 'SMF')

Calculates the stellar mass function of the given galaxies. That is, the number of galaxies at a given stellar mass.

The Model.properties["snapshot_<snapshot>"]"SMF"] array will be updated. We also split the galaxy population into “red” and “blue” based on the value of sSFRcut and update the Model.properties["snapshot_<snapshot>"]["red_SMF"] and Model.properties["snapshot_<snapshot>"]["blue_SMF"] arrays.

Parameters:

• snapshot (int) – The snapshot the SMF is being calculated at.
• plot_sub_populations (boolean, optional) – If True, calculates the stellar mass function for red and blue sub-populations.
• smf_property_name (string, optional) – The name of the property used to store the stellar mass function. Useful if different calculations are computing the stellar mass function but saving it as a different property.

sage_analysis.example_calcs.calc_SMF_history(model, gals, snapshot: int)

Calculates the stellar mass function of the given galaxies. That is, the number of galaxies at a given stellar mass.

The Model.properties["SMF"_history] array will be updated.

sage_analysis.example_calcs.calc_baryon_fraction(model, gals, snapshot: int)

Calculates the mass_baryons / halo_virial_mass as a function of halo virial mass for each baryon reseroivr (stellar, cold, hot, ejected, intra-cluster stars and black hole). Also calculates the ratio for the total baryonic mass.

The Model.properties["snapshot_<snapshot>"]["halo_<reservoir_name>_fraction_sum"] arrays are updated for each reservoir. In addition, Model.properties["snapshot_<snapshot>"]["halo_baryon_fraction_sum"] is updated.

Notes

The halo virial mass we use is the background FoF halo, not the immediate host halo of each galaxy.

We only sum the baryon mass in each stellar mass bin. When converting this to the mass fraction, one must divide by the number of halos in each halo mass bin, Model.properties["snapshot_<snapshot>"]["fof_HMF"]. See plot_baryon_fraction() for full implementation.

If the Model.properties["snapshot_<snapshot>"]["fof_HMF"] property, with associated bins Model.bins["halo_mass"bin"] have not been initialized, a ValueError is thrown.

sage_analysis.example_calcs.calc_bh_bulge(model, gals, snapshot: int)

Calculates the black hole mass as a function of bulge mass.

The number of galaxies added to Model.properties["snapshot_<snapshot>"]["BlackHoleMass"] and Model.propertiesp["snapshot_<snapshot>"]["BulgeMass"] arrays is given by sample_size weighted by number_galaxies_passed / _num_gals_all_files. If this value is greater than number_galaxies_passed, then all galaxies will be used.

Notes

We only consider galaxies with bulge mass greater than 10^8 Msun/h and a black hole mass greater than 10^5 Msun/h.

sage_analysis.example_calcs.calc_bulge_fraction(model, gals, snapshot: int)

Calculates the bulge_mass / stellar_mass and disk_mass / stellar_mass ratios as a function of stellar mass.

The Model.properties["snapshot_<snapshot>"]["fraction_bulge_sum"], Model.properties["snapshot_<snapshot>"]["fraction_disk_sum"], Model.properties["snapshot_<snapshot>"]["fraction_bulge_var"], Model.properties["snapshot_<snapshot>"]["fraction_disk_var"] arrays will be updated.

Notes

We only sum the bulge/disk mass in each stellar mass bin. When converting this to the mass fraction, one must divide by the number of galaxies in each stellar mass bin, the stellar mass function Model.properties["snapshot_<snapshot>"]["SMF"]. See plot_bulge_fraction() for full implementation.

sage_analysis.example_calcs.calc_gas_fraction(model, gals, snapshot: int)

Calculates the fraction of baryons that are in the cold gas reservoir as a function of stellar mass.

The number of galaxies added to Model.properties["snapshot_<snapshot>"]["gas_frac_mass"] and Model.properties["snapshot_<snapshot>"]["gas_frac"] arrays is given by sample_size weighted by number_spirals_passed / _num_gals_all_files. If this value is greater than number_spirals_passed, then all spiral galaxies will be used.

sage_analysis.example_calcs.calc_metallicity(model, gals, snapshot: int)

Calculates the metallicity as a function of stellar mass.

The number of galaxies added to Model.properties["snapshot_<snapshot>"]["metallicity_mass"] and Model.properties["snapshot_<snapshot>"]["metallicity"] arrays is given by sample_size weighted by number_centrals_passed / _num_gals_all_files. If this value is greater than number_centrals_passed, then all central galaxies will be used.

sage_analysis.example_calcs.calc_quiescent(model, gals, snapshot: int)

Calculates the quiescent galaxy fraction as a function of stellar mass. The galaxy population is also split into central and satellites and the quiescent fraction of these are calculated.

The Model.properties["snapshot_<snapshot>"]["centrals_MF"], Model.properties["snapshot_<snapshot>"]["satellites_MF"], Model.properties["snapshot_<snapshot>"]["quiescent_galaxy_counts"], Model.properties["snapshot_<snapshot>"]["quiescent_centrals_counts"], and Model.properties["snapshot_<snapshot>"]["quiescent_satellites_counts"] arrays will be updated.

Notes

We only count the number of quiescent galaxies in each stellar mass bin. When converting this to the quiescent fraction, one must divide by the number of galaxies in each stellar mass bin, the stellar mass function Model.properties["snapshot_<snapshot>"]["SMF"]. See plot_quiescent() for an example implementation.

sage_analysis.example_calcs.calc_reservoirs(model, gals, snapshot: int)

Calculates the mass in each reservoir as a function of halo virial mass.

The number of galaxies added to Model.properties["snapshot_<snapshot>"]["reservoir_mvir"] and Model.properties["snapshot_<snapshot>"]["reservoir_<reservoir_name>"] arrays is given by sample_size weighted by number_centrals_passed / _num_gals_all_files. If this value is greater than number_centrals_passed, then all central galaxies will be used.

sage_analysis.example_calcs.calc_sSFR(model, gals, snapshot: int)

Calculates the specific star formation rate (star formation divided by the stellar mass of the galaxy) as a function of stellar mass.

The number of galaxies added to Model.properties["snapshot_<snapshot>"]["sSFR_mass"] and Model.properties["snapshot_<snapshot>"]["sSFR_sSFR"] arrays is given by sample_size weighted by number_gals_passed / _num_gals_all_files. If this value is greater than number_gals_passed, then all galaxies with non-zero stellar mass will be used.

sage_analysis.example_calcs.calc_spatial(model, gals, snapshot: int)

Calculates the spatial position of the galaxies.

The number of galaxies added to Model.properties["snapshot_<snapshot>"]["<x/y/z>_pos"] arrays is given by sample_size weighted by number_galaxies_passed / _num_gals_all_files. If this value is greater than number_galaxies_passed, then all galaxies will be used.