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SIMBA Python Library

Run SIMBA from Python scripts.

SIMBA Python Library is a Python package that lets users manage SIMBA from circuit creation to simulation and post-processing.

Python syntax is accessible, so the library can be used for simple scripts as well as for larger automation workflows.

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Notebook workflow

Run simulations, inspect results, and document studies from a notebook-driven workflow.

Main uses of the Python Library

The Python Library makes it easier to integrate SIMBA into analysis, scripting, and post-processing workflows.

Improve your workflow

Python is a powerful tool for scientific computing, control, and automation around simulation models.

Work in notebooks or scripts

SIMBA can be used from regular Python scripts or from notebooks for interactive studies and reports.

Automate parameter sweeps

Simple Python loops make it easy to automate repeated simulations and compare results.

Use the Python ecosystem

SIMBA can be combined with tools such as NumPy and Matplotlib for calculations, plots, and post-processing.

Examples

The examples below show how SIMBA can be controlled directly from Python. 

# Load required module
from aesim.simba import Design
import matplotlib.pyplot as plt

# Create design
design = Design()
design.Name = "DC/DC - Buck Converter"
design.TransientAnalysis.TimeStep = 1e-6
design.TransientAnalysis.EndTime = 10e-3
circuit = design.Circuit

# Add devices
V1 = circuit.AddDevice("DC Voltage Source", 2, 6)
V1.Voltage = 50
SW1 = circuit.AddDevice("Controlled Switch", 8, 4)
PWM = circuit.AddDevice("Square Wave", 2, 0)
PWM.Frequency = 5000
PWM.DutyCycle = 0.5
PWM.Amplitude = 1
D1 = circuit.AddDevice("Diode", 16, 9)
D1.RotateLeft()
L1 = circuit.AddDevice("Inductor", 20, 5)
L1.Value = 1e-3
C1 = circuit.AddDevice("Capacitor", 28, 9)
C1.RotateRight()
C1.Value = 100e-6
R1 = circuit.AddDevice("Resistor", 34, 9)
R1.RotateRight()
R1.Value = 5
R1.Name = "R1"
for scope in R1.Scopes:
    scope.Enabled = True
g = circuit.AddDevice("Ground", 3, 14)

# Make connections
circuit.AddConnection(V1.P, SW1.P)
circuit.AddConnection(SW1.N, D1.Cathode)
circuit.AddConnection(D1.Cathode, L1.P)
circuit.AddConnection(L1.N, C1.P)
circuit.AddConnection(L1.N, R1.P)
circuit.AddConnection(PWM.Out, SW1.In)
circuit.AddConnection(V1.N, g.Pin)
circuit.AddConnection(D1.Anode, g.Pin)
circuit.AddConnection(C1.N, g.Pin)
circuit.AddConnection(R1.N, g.Pin)

# Run simulation
job = design.TransientAnalysis.NewJob()
status = job.Run()

# Get results
t = job.TimePoints
Vout = job.GetSignalByName("R1 - Instantaneous Voltage").DataPoints

# Plot curve
fig, ax = plt.subplots()
ax.set_title(design.Name)
ax.set_ylabel("Vout (V)")
ax.set_xlabel("time (s)")
ax.plot(t, Vout)
Create

Create and modify circuits

Circuits can be created from scratch and modified with a few lines of Python: adding devices, placing them, connecting them, running the simulation, and retrieving results.

  • Create circuits directly from code
  • Modify simulation settings and scopes
  • Retrieve and plot results
# Load required modules
import matplotlib.pyplot as plt
import numpy as np
from aesim.simba import DesignExamples

# Calculate Vout = f(duty cycle)
BuckBoostConverter = DesignExamples.BuckBoostConverter()
dutycycles = np.arange(0.00, 0.9, 0.9 / 100)
Vouts = []
for dutycycle in dutycycles:
    # Set duty cycle value
    PWM = BuckBoostConverter.Circuit.GetDeviceByName("C1")
    PWM.DutyCycle = dutycycle

    # Run calculation
    job = BuckBoostConverter.TransientAnalysis.NewJob()
    status = job.Run()

    # Retrieve results
    t = np.array(job.TimePoints)
    Vout = np.array(job.GetSignalByName("R1 - Instantaneous Voltage").DataPoints)

    # Average output voltage for t > 2 ms
    indices = np.where(t >= 0.002)
    Vout = np.take(Vout, indices)
    Vout = np.average(Vout)

    # Save results
    Vouts.append(Vout)

# Plot curve
fig, ax = plt.subplots()
ax.set_title(BuckBoostConverter.Name)
ax.set_ylabel("Vout (V)")
ax.set_xlabel("Duty Cycle")
ax.plot(dutycycles, Vouts)
Sweep

Parametric analysis

Basic Python functions such as for loops are enough to automate parameter variations and study their effect on simulation results.

  • Run repeated simulations with different values
  • Post-process results in Python
  • Generate custom plots and analyses

Analysis workflows from Python

SIMBA analyses can be launched directly from Python and integrated into larger workflows.

Python scripting workflow with SIMBA
Workflow

Improve your workflow

With the SIMBA Python Library, SIMBA simulations and pre- and post-processing can be integrated into one scriptable workflow.

  • Combine simulation and post-processing
  • Reduce manual steps
  • Build reusable scripts
AC sweep and analysis from a SIMBA notebook
Analysis

Transient, steady-state, or AC analysis

Different circuit analyses can be run from Python to obtain transient or steady-state waveforms, transfer functions, and impedances.

  • Run several types of analysis
  • Use Python for custom post-processing
  • Generate plots and reports
Automation and scripting

Use SIMBA from Python

The Python Library is useful for automation, post-processing, and repeated simulation studies around SIMBA models.

  • Create, run, and analyze from Python
  • Use notebooks or scripts
  • Works well with SIMBA Desktop
Open the documentation Explore SIMBA Desktop