instructions.md

Instructions

in the following, brief instructions on how to use the tools of the repository are provided.

tardis_opacity.py

With tardis_opacity.py, the opacity within a Tardis model may be examined in detail. To determine the line opacity, the expansion opacity formalism is used.

The following figure shows the total opacity in the first and last radial shell in the Tardis calculation using the tardis_example setup:

It has been produced with the following instructions (some plotting related instructions are omitted):

import tardis
import tardis_opacity as top

config = tardis.yaml_load("tardis_example.yml")
mdl = tardis.run_tardis(config)

# initialise opacity calculator
opacity_diag = top.opacity_calculator(mdl)
# get total opacity on a standard frequency grid for each cell
chi_tot = opacity_diag.kappa_tot
# midpoints of the frequency grid:
nu_grid = 0.5 * (opacity_diag.nu_bins[1:] + opacity_diag.nu_bins[:-1])

plt.loglog(nu_grid, chi_tot[:, 0], label = "cell #0")
plt.loglog(nu_grid, chi_tot[:, -1], label = "cell #19")

With the opacity_calculator, the following quantities may also be calculated:

• pure bound-bound opacity per shell
• pure Thomson opacity per shell
• Planck-mean opacity per shell
• Planck-mean optical depth of each shell
• Planck-mean optical depth, integrated from the ejecta surface

tardis_kromer_plot.py

With tardis_kromer_plot.py, the importance of the different chemical elements for the formation of the synthetic spectra may be illustrated. This type of illustration has been developed by M. Kromer.

Important: The virtual packet logging capability must be active in order to use this tool to generate Kromer plots from the virtual packet population. Thus, Tardis must be compiled with the flag --with-vpacket-logging.

The following figure shows such a "Kromer-type" plot for a Tardis calculation using the tardis_example setup:

It has been produced with the following instructions:

import tardis
import tardis_kromer_plot as tkp
import tardis_minimal_model as tmm

config = tardis.yaml_load("tardis_example.yml")
mdl = tardis.run_tardis(config)
minmodel = tmm.minimal_model(mode="virtual")
minmodel.from_interactive(mdl)


# initialise the plotting tool
plotter = tkp.tardis_kromer_plotter(minmodel, mode="virtual")
# generate plot
plotter.generate_plot(xlim=(3000,1e4), twinx=True)

NOTE: When using this in non-interactive mode (e.g. from a python script), you will also need to call plt.show() after the above code to display the generated plot.

If a large amount of elements are cluttering the colormap then a flag nelements can be included, e.g. plotter.generate_plot(xlim=(3000,1e4), twinx=True, nelements=10). This command will make the plot only include the n elements associated with the most line transitions; if not specified, then all elements in the model, with at least 1 transition, are included. If the flag nelements is set, it is possible to wind up with a Kromer plot with colourless regions of absorption and/or emission. This is a result of the value of nelements being too low, causing elements that have significant contributions to the spectrum to be removed. A simple fix to this is to increase the user-specified value of nelements.

A Kromer-type plot for the real packet population may be generated analogously by replacing mode='virtual' with mode='real' in the above example.:

compute_features.py

With compute_features.py, one can compute the pEW, depth and velocity of selected spectral features. The routine that define the features and the calculations are based on Silverman+ 2012. Uncertainties are computed with a simple Monte Carlo routine based on Liu+ 2016.

Important: To run compute_features.py, the PyAstronomy package has to be installed in the same environment as tardis. Currently using PyAstronomy v11.0, which can be found here.

import tardis
import compute_features as cp

sim = tardis.run_tardis('./docu/inputs/loglum-8.538.yml',
                        './docu/inputs/kurucz_cd23_chianti_H_He.h5')

#Get wavelength and flux values as arrays.
wavelength = sim.runner.spectrum_virtual.wavelength[::-1].value
flux = sim.runner.spectrum_virtual.luminosity_density_lambda[::-1].value

#Compute features
D = cp.Analyse_Spectra(wavelength=wavelength, flux=flux,
  redshift=0., extinction=-0.014, smoothing_window=17).run_analysis()

#Compute corresponding uncertainties.
D = cp.Compute_Uncertainty(D=D, smoothing_window=17,
                           N_MC_runs=1000).run_uncertainties()

#Make spectrum plot where features are highlighted.
cp.Plot_Spectra(D, './docu/images/example_spectrum.png',
                show_fig=True, save_fig=True)

print 'pEW of main Si feature (f7) = ', D['pEW_f7'], '+-', D['pEW_unc_f7']
print 'Velocity of weak Si feature (f6) = ', D['velocity_f6'], '+-',\
      D['velocity_unc_f6']

tardis_line_id.py

With tardis_line_id.py, one can determine the transition(s) that are influencing the spectrum most, within a user-specified range.

The following figure shows such an example bar chart for a Tardis calculation using the tardis_example setup:

It has been produced with the following instructions:

import tardis
import tardis_minimal_model as tmm
import tardis_line_id as tlid

config = tardis.yaml_load("model_late.yml")
mdl = tardis.run_tardis(config)
minmodel = tmm.minimal_model(mode="virtual")
minmodel.from_interactive(mdl)

# initialise the plotting tool
plotter = tlid.line_identifier(minmodel)
# generate plot
plotter.plot_summary(lam_min=4000, lam_max=7000, nlines=20, output_filename='output_barchart_info.txt')