Importing Python Functions

For functions not supported by the above equation formats, you can define a python function and link it.

Functions are stored in a separate python file, which must be imported in the Oneil file.

import <name of functions file>

That file should simply define functions matching the name used in the parameter:

import numpy as np

def temperature(transit_mode):
    ...

In the Oneil file, give the python function on the right hand of the equation, including other parameters as inputs:

Temperature: T = temperature(D) :K

Fallback Calculations

Python functions may have dependencies that aren’t always available, or may take a long time to run. You can specify a fallback calculation using the ? operator. If the Python function fails (e.g., missing dependencies, runtime errors), Oneil will use the fallback and warn the user:

Temperature: T = expensive_simulation(D) ? D * 0.5 + 273 :K

In this example, if expensive_simulation fails, Oneil will calculate D * 0.5 + 273 instead and display a warning that the Python function should be run for greater accuracy.

This is particularly useful for:

• Sharing models with users who may not have all Python dependencies installed
• Providing quick approximations during iterative design
• Graceful degradation when simulations fail

Function Caching

Note

This is not currently implemented in the Rust implementation but will be implemented soon.

Python function results are automatically cached to avoid re-running expensive calculations. The cache:

• Persists across REPL sessions - Close and reopen Oneil, cached results remain
• Is version-controllable - Stored as one cache file per model under __oncache__/
• Is shareable - Other users can use cached results even without Python dependencies
• Is human-readable - Each model cache stores the simulation function, simulation file, and parameter input/output snapshots (min, max, units) as JSON for cleaner git diffs
• Only rewrites changed entries - Re-running Oneil leaves the cache file untouched unless a simulation’s latest cached inputs or output changed
• Auto-invalidates when imported Python source files, their local Python dependencies, or the simulation inputs change

Use the cache command in the CLI to view cache statistics or cache clear to clear it.