SIMBA Python Library

SIMBA python library is a Python package which allows the user to manage SIMBA (from circuit creation to simulation and post-processing) with Python scripts. As python syntax is very accessible, no python experience is required to use this library.

Improve your workflow

Thanks to the impressive number of modules for scientific computing, control and machine learning, python is a powerful tool.

With the SIMBA python library, it allows the user to integrate simba simulations and pre- and post-processing like never before: it can somehow replace the classic user files made with excel or Matlab® that come with the circuit simulation files!

        #  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)
        L1 = circuit.AddDevice("Inductor", 20, 5)
        L1.Value = 1E-3
        C1 = circuit.AddDevice("Capacitor", 28, 9)
        C1.Value = 100E-6
        R1 = circuit.AddDevice("Resistor", 34, 9)
        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_ylabel('Vout (V)')
        ax.set_xlabel('time (s)')

Create and Modify Circuits

Circuits can be created from scratch and modified with a few command lines: adding, placing and connecting components are easily done.

Transient, steady-state or AC analysis

Different circuit analysis can be performed to get transient or steady-state waveforms with respectively transient or steady-state analysis or to get transfer functions and impedances with the AC analysis.

        # Load required modules
        import matplotlib.pyplot as plt
        import numpy as np
        from aesim.simba import DesignExamples
        # Calculating Vout=f(dutycycle)
        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')
            # 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 > 2ms
            indices = np.where(t >= 0.002)
            Vout = np.take(Vout, indices)
            Vout = np.average(Vout)
            # Save results
        # Plot Curve
        fig, ax = plt.subplots()
        ax.set_ylabel('Vout (V)')
        ax.set_xlabel('Duty Cycle')

Parametric Analysis

Thanks to basic python functions such a 'FOR' loop, performing a parametric variation to get the influence of this parameters on the simulation is incredibly easy !