Python Scripts

scripts/plot_asloth.py

scripts.plot_asloth.plot_comparison(UserFolder, UserLabels=None)

Function to plot results from A-SLOTH and compare them to literature or other runs.

We use the most recent folder to plot and compare to literature. If one or more folders are specified, they will all be plotted. This can also be used to plot just one specific folder.

Parameters

UserFolder (list[str]) – List of folder names to be plotted.
UserLabels (list[str]) – List of labels. If none, UserFolders are used as labels

Returns: Beautiful plots

scripts.plot_asloth.readMWproperties(UserFolder)

scripts/tau/plot_Vion.py

tau.plot_Vion.plot_Vion(UserFolder, UserLabels, folder_output)

Function to plot ionisation histories and calculate tau

Parameters

UserFolder (List[str]) – List of folder names to be plotted
UserLabels (List[str]) – List of labels. If none, UserFolders are used as labels
folder_output (str) – Folder in which plots will be saved

Returns

tau0 (float) – optical depth to Thomson scattering
tau_sigma (float) – tau uncertainty

tau.plot_Vion.get_tau_sigma(folder)

Function to calculate tau without plotting ionisation history

Parameters

folder (str) – Folder in which plots will be saved

Returns

tau0 (float) – optical depth to Thomson scattering
tau_sigma (float) – tau uncertainty

scripts/wrapper

This script allows to explore input parameters by automatically launching various runs. Users can set their specific path_output variable. If you want to run with NBODY merger trees, also set path_tree. If you think that disk I/O might be a bottleneck, you should also set your username in the function N_disk_sleep() to work correctly.

wrapper.loop_trees.RunTrees(cats, NBODY)

This function loops over a set of merger trees and explores one combination of input parameters

We first define input parameters that are constant within this loop. Then, we define tree-specific input parameters. Then, we check if sufficient computing resources are available. Eventually, we launch the A-SLOTH run.

Parameters

cats (list) – catalogue of merger trees [[name, nlev, RAM]]
NBODY (bool) – N-Body or EPS-generated merger trees

wrapper.loop_trees.Get_CatList(NBODY=False, N=3)

Function to generate list of catalogues to loop over

Parameters

NBODY (bool) – N-Body or EPS-generated merger trees
N (int) – number of random realisations in case of EPS-generated trees

Returns

cats – list of lists of type [[name, nlev, RAM]]

Return type

list

wrapper.loop_trees.avail_mem_GB(): Funtion returns available RAM in GB

wrapper.loop_trees.CPU_use(): Funtion returns available CPUs in %

wrapper.loop_trees.N_disk_sleep()

Funtion returns number of processes that are idle due to I/O.

Change username accordingly

wrapper.analyse_trees.get_p_from_folders(dir_prefix, ifEPS=True)

Function that calculates the p-value (and additional values).

Based on the directory prefix for the A-SLOTH output folder that should be analysed Function will you all folders that match this directory prefix.

Parameters: dir_prefix (str) – directory prefix that should be used to search for data
Returns: fit_asloth – class object that contains fit results
Return type: asloth_fit

wrapper.tutorial_GridPlot.plot_tutorial2()

Plotting script to visualise results of the two scripts loop_scripts.py and analysis_trees.py.

The plot layout is not optimized for aesthetics, but it should simply demonstrate the versatility of A-SLOTH. The resulting plot is a modified version of Fig. 17 in Hartwig+22

scripts/tau/plot_tgas.py

utilities.plot_tgas.plot_tgas(output_folder)

Illustrates time evolution of different baryonic quantities based on output file t_gas_substeps.dat. outout_folder has to be set manually.

Parameters: output_folder (str) – folder that contains results of run that should be analysed

scripts/utility.py

class utilities.utility.asloth_config

container class for writing config namelist files for ASLOTH

set_config_to_default()

sets default parameters for the main configuration

Parameters: self (asloth_config) – the configuration to be restored to defaults

set_metals(metals)

sets metal configuration for a specified list of elements

Parameters

self (asloth_config) – configuration
metals (list) – list of strings of the elements to be traced

to_file(fname)

class utilities.utility.asloth_fit

class for handling fitting parameters and results of A-SLOTH

get_p(): ‘ Calculates the goodness-of-fit parameters based on 6 different observables. Please see Hartwig+22 for more details.

print_fit(): ‘ Prints all class properties

set_fit_to_default()

sets default parameters for the fit object

Parameters: self (asloth_fit) – set all parameters to default values

utilities.utility.get_p_from_sigma(sigma): Get p-value from “how many sigma away”

utilities.utility.write_namelist(f, list_name, var_dict)

function to write fortran namelists

Parameters

f (file) – opened file to write the namelist to
list_name (str) – name of the namelist. Note that this must be the exact name used in the fortran code
var_dict (dictionary) – dictionary of variables to be written to the file

scripts/SMHM/plot_scatterSMHM.py

SMHM.plot_scatterSMHM.plot_scatterSMHM(UserFolder, UserLabels, folder_output)

Function to read data and plot scatter SMHM relation.

If one or more folders are specified, they will all be plotted. This can also be used to plot just one specific folder.

Parameters

UserFolder (str) – List of folder names to be plotted.
UserLabels (str) – List of labels. If none, UserFolders are used as labels.
folder_output (str) – where the plot is saved.

Returns

None

SMHM.plot_scatterSMHM.plot_CumuSMF(UserFolder, UserLabels, folder_output, if_plot)

Function to return ks statistics, p-values, and MW stellar masses

If one or more folders are specified, they will all be plotted. This can also be used to plot just one specific folder.

Parameters

UserFolder (str) – List of folder names to be plotted.
UserLabels (str) – List of labels. If none, UserFolders are used as labels.
folder_output (str) – where the plot is saved.

Returns

Lists of ks statistic, p-value and MW stellar mass.

SMHM.plot_scatterSMHM.read_data(UserFolder, UserLabels, folder_output)

Function to read data to plot SMHM relation

If one or more folders are specified, they will all be plotted. This can also be used to plot just one specific folder.

Parameters

UserFolder (str) – List of folder names to be plotted.
UserLabels (str) – List of labels. If none, UserFolders are used as labels
folder_output (str) – where the plot is saved

Returns

Virial masses and stellar masses of galaxies in pandas.DataFrame format

SMHM.plot_scatterSMHM.M_AM(M)

Function to compute expected stellar mass of the galaxy at given virial mas (Garrison-Kimmel et al. 2014).

Parameters: M (float) – virial mass of the halo
Returns: expected stellar mass

SMHM.plot_scatterSMHM.f_bwc(x)

SMHM.plot_scatterSMHM.AMtwodhistogram(df_list, y0, p_dir, output_name, WhichMass, climmax, if_imfana)

The actual function to plot scatter SMHM relation.

Parameters

df_list (list of pd.DataFrame) – input data
y0 (float) – the observational completeness
p_dir (str) – output folder

Returns:

SMHM.plot_scatterSMHM.CumulativeNumberOfSatellites(df_list, output_name, WhichMass, if_plot, p_dir)

The actual function to plot cumulative stellar mass function and compute ks statistic, p-value from the KS test.

Parameters

df_list (list of pd.DataFrame) – input data
output_name (str) – figure name
WhichMass (float) – which stellar mass to use (the total or survival)
if_plot (bool) – whether to make plots or not
p_dir (str) – output folder

Returns

lists of ks statistic, p-value, MW stellar mass

scripts/SMHM/plot_binnedSMHM.py

SMHM.plot_binnedSMHM.plot_binnedSMHM(UserFolder, UserLabels, folder_output)

The function reads data from multiple folders and plot the binned SMHM relations

If one or more folders are specified, they will all be plotted as separate SMHM relations. This can also be used to plot just one specific folder.

Parameters

UserFolder (str) – List of folder names to be plotted.
UserLabels (str) – List of labels. If none, UserFolders are used as labels
folder_output (str) – where the plot is saved

Returns

plot of SMHM relation comparison

SMHM.plot_binnedSMHM.IMFANA(AMBound, MX, MAMX, imf_list, y0, p_dir, imfname, WhichMass, climmax, cps, cs, ilabel, fname)

The function plots binned SMHM relation.

Parameters

AMBound – boundary of the SMHM relation from abundance matching (AM)
MX – virial masses
MAMX – stellar masses obtained from the AM technique at given virial masses
imf_list (list of pd.DataFrame) – input data
y0 (float) – observational completeness
p_dir (str) – output folder

Returns

plot of SMHM relation

SMHM.plot_binnedSMHM.AM_BinMeanstd(df_list, WhichMass, tname, index_data, c=None)

The actual function to plot binned SMHM relation.

Parameters

df_list (list of pd.DataFrame) – input data
WhichMass (float) – which stellar mass to use (the total or survival)
tnmae (str) – labels
index-data – index of the data
c – specific color for the SMHM relation

Returns

adding a binned SMHM relation to the plot

SMHM.plot_binnedSMHM.PlotNadler20SMHM(): The function plots SMHM relation from Nadler et al. 2020.

scripts/plot_mcrit.py

utilities.plot_mcrit.plot_mcrit()

Function to plot ciritical mass of a halo at certain redshift, given by different models, and at different initial streaming velocities.

Returns: Figure that shows M_crit v.s. z at different initial streaming velocities and from different models

utilities.plot_mcrit.crit_mass(z, lw_mode, VBC)

Function to obtain ciritical mass of a halo at certain redshift, given by different models

We use lw_mode=5 (Schauer et al. 2021) as the fiducial model. Use can also choose lw_mode=4, which is a combined model from O’Shea et al. 2008, Stacy et al. 2011, and Hummel et al. 2012. lw_mode=7 corresponds to a model from Fialkov et al. 2013.

Parameters

z (float) – redshift
lw_mode (int) – which M_crit model
VBC (float) – initial streaming velocity, in units of sigma_v

Returns

Critical mass of a halo to have star formation

scripts/plot_stellarprop.py

utilities.plot_stellarprop.plot_stellarproperties()

Function to reproduce the stellar properties plot

Returns: Stellar masses v.s. stellar properties, e.g. stellar lifetime, carbon yields, iron yields, supernova energie.