FAQ

General Questions

What is SIMBA?

SIMBA is a Python software package that numerically solves time-dependent chemical reaction networks for astrophysical environments. The code is can be used for modeling complex chemical evolution in settings such as the interstellar medium (ISM), molecular clouds, and protoplanetary disks.

How does SIMBA compare to other astrochemistry codes?

SIMBA is a complementary tool designed to work alongside existing higher-dimensional chemical models, offering an efficient way to explore parameter space. The code maintains full compatibility with the established DALI format, enabling users to apply existing chemical networks without any reformatting or conversion requirements. This compatibility allows SIMBA to serve as a rapid exploration tool for complex codes like DALI, facilitating quick analysis of parameter dependencies and extraction of key information such as reaction rates and pathways. A comprehensive benchmarking comparison between SIMBA and DALI is detailed in our code overview paper.

What physical processes does SIMBA model?

SIMBA models a comprehensive set of physical processes, including:

Gas-phase chemistry (neutral-neutral, ion-neutral, etc.)
Photochemistry (including self-shielding)
Cosmic ray chemistry
X-ray chemistry
PAH chemistry
Grain-surface processes (freeze-out, desorption, hydrogenation)
H₂ formation and chemistry

Installation & Setup

Is SIMBA available via pip?

Yes. SIMBA can be installed from the command line via:

pip install simba_chem

What are the system requirements for running SIMBA?

SIMBA requires:

Python 3.8 or higher
NumPy
SciPy
Matplotlib
Numba (for just-in-time compilation of performance-critical routines)

Additionally, the GUI requires Node.js to be preinstalled.

What are the computational requirements for SIMBA?

SIMBA is designed to be lightweight and can run on standard desktop or laptop hardware. For most applications with networks containing ~100-200 species and ~1000-2000 reactions, a modern dual-core processor with 4GB RAM is sufficient. Larger networks or longer timescales may benefit from additional computational resources. The GUI requires minimal additional resources beyond the core solver. Typical runtime is 5-30 seconds.

Chemical Networks

Can I use my own chemical network with SIMBA?

Yes, but you will need ensure it is in the DALI format. Alternatively, the modular structure makes it relatively straightforward to implement your own parser for different network formats. See the “Alternative Network Formats” section in the documentation for details.

How do I add new reactions to the network?

To add new reactions to the network, you’ll need to edit the chemical network file, ensuring you follow the correct format:

Each reaction should have properly defined reactants and products
Specify the appropriate reaction type code
Include all required rate coefficient parameters (α, β, γ)
Define valid temperature ranges

What reaction types are supported?

SIMBA supports numerous reaction types including standard gas-phase reactions, photodissociation (with and without self-shielding), cosmic ray chemistry, X-ray chemistry, PAH reactions, grain chemistry, and H₂* chemistry. See the “Reaction Types” section in the documentation for the complete list.

Running Simulations

How can I improve convergence for difficult models?

If your model is having convergence issues, try these approaches:

Check that your chemical network is ‘closed’ (ie. all species have formation and destruction rates)
Simplify your chemical network by removing unimportant reactions
Slightly peturb input parameter values
Relax the tolerances (atol and rtol) that are hard-coded into the core.py module (at your own risk!)

How do I interpret the output from SIMBA?

The terminal output provides a summary of the model parameters, integration statistics, and key results like the most abundant species and dominant reactions. More detailed results are stored in the returned solution object, which can be accessed for custom analysis or visualized using the built-in Analysis module.

Can SIMBA handle time-dependent physical conditions?

The current version of SIMBA is designed for fixed physical conditions. However, time-dependent evolution can be approximated by running a series of models with different conditions and using the final state of each model as the initial conditions for the next.

How do I turn of the output in the console?

Set the verbosity to False immediately after creating a SIMBA instance:

network = simba.Simba()     
network.set_verbosity(False)
# Then run the code as normal

Visualization and Analysis

How do I create custom plots of my results?

The Analysis module provides basic plotting routines, but you can also access the raw data in the solution object to create custom visualizations:

# Access the raw data
time = network.time  # time points in seconds
abundances = network.species.abundance  # species abundances over time

# Create custom plots
import matplotlib.pyplot as plt
plt.figure(figsize=(10, 6))
plt.loglog(time/3.15e7, abundances[:, species_index], 'b-')  # convert to years
plt.xlabel('Time (years)')
plt.ylabel('Abundance')
plt.title(f'Abundance of {species_name} over time')
plt.grid(True, which="both", ls="-")
plt.show()

Can I export my results for use in other tools?

Yes, you can export the results to standard formats like CSV for further analysis:

# Export abundances to CSV
analysis = simba.Analysis(network)
analysis.export_abundance_data('abundances.csv')

Support and Contribution

How do I report a bug or request a feature?

Please email l.keyte@qmul.ac.uk with a detailed description of the bug or feature request. Include information about your system, SIMBA version, and any relevant code snippets or input files.

How can I contribute to SIMBA?

We welcome contributions! Please contact us to discuss how you might contribute to the project.

What features are planned in future releases

We plan to incorporate a range of new features into the next release, including:

Steady-state solver option
Full treatment of photodissociation using UV cross sections
Option to adopt user-defined C, N2, and CO column densities for self-shielding
Additional visualisation tools

Where can I find more examples of SIMBA in use?

Examples of SIMBA applications can be found in our code paper (citation in the documentation)

Miscellaneous

What is LukeNet?

The LukeNet module is available to SIMBA platinum users.