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Python electromagnetic co-simulation library

Project description


CoSimPy is an open source Python library aiming to combine results from electromagnetic (EM) simulation with circuits analysis through a cosimulation environment.


Getting Started

The library has been developed with Python 3.7 and successfully tested down to Python 3.5 up to Python 3.10 on Linux, Windows and macOS


The library uses the follwong additional packages:

The package versions reported in brackets represent the oldest releases with which the library has been succesfully tested.


With pip:

pip install cosimpy


After installation, the library can be imported as:

import cosimpy


A basic example

In the following example, a 1-port RF coil is modeled as a 5 ohm resistance in series with a 300 nH inductance. The RF coil is supposed to generate a 0.1 μT magnetic flux density oriented along the y-direction when it is supplied with 1 W incident power at 128 MHz. The coil is connected to a tuning/matching network through a 5 cm long lossless transmission line. The network is designed to transform the impedance at its output to 50 ohm at 128 MHz.

import numpy as np
import cosimpy

L_coil = 300e-9 #Coil inductance
R_coil = 5 #Coil resistance

#Frequency values at which the S parameters are evaluated
frequencies = np.linspace(50e6,250e6,1001)

#Number of points along x-, y-, z-direction where the magnetic flux density is evaluated
nPoints = [20,20,20] 

#b_field is evaluated at one frequency (128 MHz) at one port
b_field = np.zeros((1,1,3,
#Only the y-component is different from zero 
b_field[:,:,1,:] = 0.1e-6 

#S_Matrix instance to be associated with the RF coil instance
s_coil = cosimpy.S_Matrix.sMatrixRLseries(R_coil,L_coil,frequencies) 
#EM_Field instance defined at 128 MHz to be associated with the RF coil instance
em_coil = cosimpy.EM_Field([128e6], nPoints, b_field)

#RF_Coil instance
rf_coil = cosimpy.RF_Coil(s_coil,em_coil) 

#The average value of the y-component of the magnetic flux density


#5 cm, 50 ohm, lossless transmission line
tr_line = cosimpy.S_Matrix.sMatrixTrLine(5e-2,frequencies) 

#Connection between the RF coil and the transmission line
rf_coil_line = rf_coil.singlePortConnRFcoil([tr_line],True) 

#To design the tuning/matching network, I need to know the impedance value at 128 MHz


#The impedance can be transormed to 50 ohm at 128 MHz deploying a T-network made of two capacitors and one inductor with the following values:

Ca = 1.87e-12 #farad
Cb = 27.24e-12 #farad
L = 56.75e-9 #henry

#I create the S_Matrix instances associated with Ca, Cb and L
S_Ca = cosimpy.S_Matrix.sMatrixRCseries(0,Ca,frequencies)
S_Cb = cosimpy.S_Matrix.sMatrixRCseries(0,Cb,frequencies)
S_L = cosimpy.S_Matrix.sMatrixRLseries(0,L,frequencies)

#I create the S_Matrix instance of the tuning/matching network. 
tun_match_network = cosimpy.S_Matrix.sMatrixTnetwork(S_Ca,S_L,S_Cb)

#The RF coil is connected to the matching network. The capacitor Ca will be in series with the transmission line
rf_coil_line_matched = rf_coil_line.singlePortConnRFcoil([tun_match_network], True) 

#The average value of the y-component of the magnetic flux density


#Print the S11 parameter

CoSimPy demonstration

In the "CoSimPy-Demo.ipynb" script inside the "examples/cosimpy_demonstration" folder, some of the CoSimPy feature are shown with reference to the case study considered here.


For testing the library, pytest is required.
After installing CoSimPy, download the "test" folder and, from a terminal, execute:

cd path_to_test_folder/test
pytest -v

Different tests can be enabled/disabled through the relevant boolean flags in

Slower tests can be skipped adding the --runfast option in the terminal

To test the library under different Operating Systems and/or Python versions, tox can be exploited. Just download the whole CoSimPy distribution and, from a terminal, execute:

cd cosimpy_folder
tox -vr

where cosimpy_folder is the folder containing the tox.ini file


This project is licensed under the MIT License - see the LICENSE file for details.

Related Publications

If you find CoSimPy useful for your work, please consider to cite this paper!


The library has been developed in the framework of the Researcher Mobility Grant (RMG) associated with the european project 17IND01 MIMAS. This RMG: 17IND01-RMG1 MIMAS has received funding from the EMPIR programme co-financed by the Participating States and from the European Union's Horizon 2020 research and innovation programme.

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