pysimlin 0.1.3

Python bindings for the Simlin system dynamics simulation engine

pysimlin - Python bindings for Simlin

Python bindings for the Simlin system dynamics simulation engine.

Features

• Load models from XMILE, Vensim MDL, and binary protobuf formats
• Run system dynamics simulations with full control
• Get simulation results as pandas DataFrames
• Analyze model structure and feedback loops
• Edit existing models or build new ones programmatically via Python context managers
• Full type hints for IDE support
• Loops That Matter (LTM) analysis for feedback loop importance

Installation

pip install pysimlin

Note: Install with pip install pysimlin but import with import simlin.

Requirements

• Python 3.11 or higher
• numpy >= 1.22.0
• pandas >= 1.5.0
• cffi >= 1.15.0

Quick Start

import simlin
from pathlib import Path

# Load a model from file (auto-detects format)
project = simlin.Project.from_file("model.stmx")

# Get the default model
model = project.get_model()

# Create and run a simulation
sim = model.new_sim()
sim.run_to_end()

# Get results as a pandas DataFrame
results = sim.get_results()
print(results.head())

# Access individual variables
population = sim.get_series("population")

Examples

Editing a flow in an existing model

"""Example showing how to edit an existing model's flow equation with pysimlin."""

from __future__ import annotations

import simlin


EXAMPLE_XMILE = b"""<?xml version='1.0' encoding='utf-8'?>
<xmile version=\"1.0\" xmlns=\"http://docs.oasis-open.org/xmile/ns/XMILE/v1.0\" xmlns:isee=\"http://iseesystems.com/XMILE\" xmlns:simlin=\"https://simlin.com/XMILE/v1.0\">
  <header>
    <name>pysimlin-edit-example</name>
    <vendor>Simlin</vendor>
    <product version=\"0.1.0\" lang=\"en\">Simlin</product>
  </header>
  <sim_specs method=\"Euler\" time_units=\"Year\">
    <start>0</start>
    <stop>80</stop>
    <dt>0.25</dt>
  </sim_specs>
  <model name=\"main\">
    <variables>
      <stock name=\"population\">
        <eqn>25</eqn>
        <inflow>net_birth_rate</inflow>
      </stock>
      <flow name=\"net_birth_rate\">
        <eqn>fractional_growth_rate * population</eqn>
      </flow>
      <aux name=\"fractional_growth_rate\">
        <eqn>maximum_growth_rate * (1 - population / carrying_capacity)</eqn>
      </aux>
      <aux name=\"maximum_growth_rate\">
        <eqn>0.10</eqn>
      </aux>
      <aux name=\"carrying_capacity\">
        <eqn>1000</eqn>
      </aux>
    </variables>
  </model>
</xmile>
"""


def run_simulation(model: simlin.Model) -> float:
    """Run the model to the configured stop time and return the ending population."""

    with model.new_sim() as sim:
        sim.run_to_end()
        return float(sim.get_value("population"))


def main() -> None:
    """Demonstrate editing a flow equation and verify the change takes effect."""

    project = simlin.Project.from_xmile(EXAMPLE_XMILE)
    model = project.get_model()

    baseline_final = run_simulation(model)

    with model.edit() as (current, patch):
        flow_var = current["net_birth_rate"]
        flow_var.flow.equation.scalar.equation = (
            "fractional_growth_rate * population * 1.5"
        )
        patch.upsert_flow(flow_var.flow)

    accelerated_final = run_simulation(model)

    if not accelerated_final > baseline_final + 10:
        raise RuntimeError(
            "Edited model did not accelerate growth as expected: "
            f"baseline={baseline_final:.2f} accelerated={accelerated_final:.2f}"
        )

    print(
        "Updated growth equation increased the final population from "
        f"{baseline_final:.1f} to {accelerated_final:.1f}."
    )


if __name__ == "__main__":
    main()

Building a logistic population model programmatically

"""Create a new Simlin project and build a simple population model using pysimlin's edit API."""

from __future__ import annotations

import simlin
import simlin.pb as pb


def build_population_project() -> simlin.Project:
    """Return a project containing a logistic population model created via model.edit()."""

    project = simlin.Project.new(
        name="pysimlin-population-example",
        sim_start=0.0,
        sim_stop=80.0,
        dt=0.25,
        time_units="years",
    )

    project.set_sim_specs(
        start=0.0,
        stop=80.0,
        dt={"value": 0.25},
        time_units="years",
    )

    model = project.get_model()
    with model.edit() as (_, patch):
        population = pb.Variable.Stock()
        population.ident = "population"
        population.equation.scalar.equation = "50"
        population.inflows.extend(["births"])
        population.outflows.extend(["deaths"])
        patch.upsert_stock(population)

        births = pb.Variable.Flow()
        births.ident = "births"
        births.equation.scalar.equation = "population * birth_rate"
        patch.upsert_flow(births)

        deaths = pb.Variable.Flow()
        deaths.ident = "deaths"
        deaths.equation.scalar.equation = "population * birth_rate * (population / 1000)"
        patch.upsert_flow(deaths)

        birth_rate = pb.Variable.Aux()
        birth_rate.ident = "birth_rate"
        birth_rate.equation.scalar.equation = "0.08"
        patch.upsert_aux(birth_rate)

    return project


def validate_population_curve(values: list[float]) -> None:
    """Ensure the generated population series shows logistic (S-shaped) growth."""

    if len(values) < 3:
        raise RuntimeError("Population series is unexpectedly short")

    if any(b < a for a, b in zip(values, values[1:])):
        raise RuntimeError("Population should not decline in this model")

    initial = values[0]
    mid = values[len(values) // 2]
    last = values[-1]
    growth_first_half = mid - initial
    growth_second_half = last - mid

    if not growth_first_half > 0:
        raise RuntimeError("Population failed to grow early in the simulation")

    if not growth_second_half > 0:
        raise RuntimeError("Population failed to grow late in the simulation")

    if not growth_second_half < growth_first_half:
        raise RuntimeError("Logistic growth should slow over time")

    if not 950 <= last <= 1025:
        raise RuntimeError(
            "Population should approach the carrying capacity (~1000), "
            f"but ended at {last:.2f}"
        )


def main() -> None:
    """Build, simulate, and validate the population model."""

    project = build_population_project()
    errors = project.get_errors()
    if errors:
        raise RuntimeError(f"Generated project contains validation errors: {errors}")

    model = project.get_model()
    with model.new_sim() as sim:
        sim.run_to_end()
        population_series = [float(value) for value in sim.get_series("population")]

    validate_population_curve(population_series)

    print(
        "Population grows from "
        f"{population_series[0]:.1f} to {population_series[-1]:.1f}, forming an S-shaped trajectory."
    )


if __name__ == "__main__":
    main()

Both examples live under src/pysimlin/examples/ and are executed by scripts/pysimlin-tests.sh.

API Reference

Creating Projects

# Create a new project with a blank model
project = simlin.Project.new(name="my-project")
model = project.get_model()  # defaults to the root model "main"

The engine currently expects the root model to be named main.

Loading Models

# Load from different formats
project = simlin.Project.from_xmile(xmile_bytes)  # XMILE/STMX format
project = simlin.Project.from_mdl(mdl_bytes)      # Vensim MDL format
project = simlin.Project.from_protobin(pb_bytes)  # Binary protobuf format

# Auto-detect format from file extension
project = simlin.Project.from_file("model.stmx")  # .stmx, .mdl, .pb, etc.

# Context manager for automatic cleanup
with simlin.Project.from_file("model.stmx") as project:
    model = project.get_model()
    # Project is automatically cleaned up when exiting the context

Working with Projects

# Get model information
model_names = project.get_model_names()

# Access models
model = project.get_model()           # Get default/main model
model = project.get_model("submodel") # Get specific model by name

# Check for compilation errors
errors = project.get_errors()
if errors:
    for error in errors:
        print(f"{error.code.name}: {error.message}")

Model Analysis

# Get model structure
var_names = model.get_var_names()
var_count = model.get_var_count()

# Analyze variable dependencies
incoming_deps = model.get_incoming_links("population")  # Variables that affect "population"

# Get causal links
links = model.get_links()
for link in links:
    print(f"{link.from_var} --{link.polarity}--> {link.to_var}")

Model Editing

import simlin.pb as pb

# Edit existing model variables using context manager
with model.edit() as (current, patch):
    # Access current variables by name
    stock_var = current["population"]

    # Modify the variable's properties
    stock_var.stock.equation.scalar.equation = "100"  # Change initial value

    # Apply the change
    patch.upsert_stock(stock_var.stock)

# Create new variables programmatically
with model.edit() as (current, patch):
    # Create a new auxiliary variable
    new_aux = pb.Variable.Aux()
    new_aux.ident = "growth_rate"
    new_aux.equation.scalar.equation = "0.05"
    patch.upsert_aux(new_aux)

    # Create a new flow variable
    new_flow = pb.Variable.Flow()
    new_flow.ident = "births"
    new_flow.equation.scalar.equation = "population * growth_rate"
    patch.upsert_flow(new_flow)

Running Simulations

# Create simulation
sim = model.new_sim()                # Standard simulation
sim = model.new_sim(enable_ltm=True) # Enable Loops That Matter

# Run simulation
sim.run_to_end()        # Run to final time
sim.run_to(50.0)        # Run to specific time

# Reset to run again
sim.reset()
sim.run_to_end()

# Context manager for automatic cleanup
with model.new_sim() as sim:
    sim.run_to_end()
    results = sim.get_results()

Accessing Results

# Get results as pandas DataFrame
df = sim.get_results()                    # All variables
df = sim.get_results(variables=["x", "y"]) # Specific variables

# Get individual time series as numpy arrays
values = sim.get_series("population")

# Get current value (at current simulation time)
current_val = sim.get_value("population")

# Get metadata
step_count = sim.get_step_count()

Protobuf Types

The protobuf message types are directly accessible via the pb module:

import simlin.pb as pb

# Create protobuf messages for model editing
stock = pb.Variable.Stock()
flow = pb.Variable.Flow()
aux = pb.Variable.Aux()

# Access protobuf enums
sim_method = pb.SimMethod.EULER  # or RUNGE_KUTTA_4

# Create complex structures
project = pb.Project()
model = pb.Model()

Model Interventions

# Set initial values before running
sim.set_value("initial_population", 1000)
sim.run_to_end()

# Mid-simulation interventions
sim.run_to(10)
sim.set_value("growth_rate", 0.05)
sim.run_to_end()

Feedback Loop Analysis

# Get all feedback loops
loops = project.get_loops()
for loop in loops:
    print(f"Loop {loop.id} ({loop.polarity}): {' -> '.join(loop.variables)}")
    
# Check if variable is in a loop
for loop in loops:
    if loop.contains_variable("population"):
        print(f"Population is in loop {loop.id}")

Loops That Matter (LTM)

# Run simulation with LTM enabled
sim = model.new_sim(enable_ltm=True)
sim.run_to_end()

# Get links with importance scores over time
links = sim.get_links()
for link in links:
    if link.has_score():
        print(f"{link.from_var} -> {link.to_var}")
        print(f"  Average score: {link.average_score():.4f}")
        print(f"  Max score: {link.max_score():.4f}")

# Get relative loop scores
loops = project.get_loops()
if loops:
    loop_scores = sim.get_relative_loop_score(loops[0].id)

Model Export

# Export to different formats
xmile_bytes = project.to_xmile()    # Export as XMILE
pb_bytes = project.serialize()      # Export as protobuf

# Save to file
Path("exported.stmx").write_bytes(xmile_bytes)
Path("model.pb").write_bytes(pb_bytes)

Error Handling

from simlin import (
    SimlinError,
    SimlinImportError,
    SimlinRuntimeError,
    SimlinCompilationError,
    ErrorCode
)

try:
    project = simlin.Project.from_file("model.stmx")
except SimlinImportError as e:
    print(f"Import failed: {e}")
    if e.code == ErrorCode.XML_DESERIALIZATION:
        print("Invalid XML format")

# Check for compilation errors
errors = project.get_errors()
for error in errors:
    print(f"{error.code.name} in {error.model_name}/{error.variable_name}")
    print(f"  {error.message}")

Complete Example

import simlin
import pandas as pd
import matplotlib.pyplot as plt

# Load and run a population model
with simlin.Project.from_file("population_model.stmx") as project:
    model = project.get_model()
    
    # Run baseline simulation
    with model.new_sim() as sim:
        sim.run_to_end()
        baseline = sim.get_results()
    
    # Run intervention scenario
    with model.new_sim() as sim:
        sim.set_value("birth_rate", 0.03)
        sim.run_to_end()
        intervention = sim.get_results()
    
    # Compare results
    fig, ax = plt.subplots()
    ax.plot(baseline.index, baseline["population"], label="Baseline")
    ax.plot(intervention.index, intervention["population"], label="Intervention")
    ax.set_xlabel("Time")
    ax.set_ylabel("Population")
    ax.legend()
    plt.show()

Supported Platforms

• macOS (ARM64)
• Linux (ARM64, x86_64)

License

Apache License 2.0

Development

For development setup and contribution guidelines, see the main Simlin repository.

Running Tests

cd src/pysimlin
pip install -e ".[dev]"
pytest

Building from Source

cd src/pysimlin
python -m build