cosimpy 2.0.0
Python electromagnetic co-simulation library
Requires Python: >=3.5

# CoSimPy

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

The library is built on three classes:
* `S_Matrix` class : Management of S Matrix operations
* `EM_Field` class : Management of Electromagnetic Field (EM) operations
* `RF_Coil` class : Interface between S_Matrix and RF_Coil classes

These three classes represent the core of CoSimPy and are included in a larger framework designed to manage custom exceptions and external contributions thorugh a testing pipeline involving  [pytest](https://docs.pytest.org/en/stable/contents.html) and [tox](https://tox.wiki/en/latest/). 

![](./docs/images/framework.png)

## Summary

  - [Getting Started](#getting-started)
  - [Deployment](#deployment)
  - [Test](#test)
  - [License](#license)
  - [Related Publications](#related-publications)
  - [Acknowledgments](#acknowledgments)

## 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

### Prerequisites

The library uses the following additional packages:

- [numpy](https://numpy.org/) (>=2.0.0)
- [matplotlib](https://matplotlib.org/) (>=3.9.0)
- [h5py](https://www.h5py.org/) (>=3.1..0)
- [scipy](https://www.scipy.org/) (>=1.14.0)

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

### Installing

With [pip](https://pypi.org/project/cosimpy/):
```
pip install cosimpy
```

## Deployment

After installation, the library can be imported as:

```python
import cosimpy
```

### Examples

**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 &mu;T 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. 

```python
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,np.prod(nPoints)))
#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
np.average(np.abs(rf_coil.em_field.b_field[0,0,1,:])).round(10)

'''
Out:
    1e-07
'''

#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
rf_coil_line.s_matrix[128e6].getZMatrix()

'''
Out:
    array([[[41.66705459+708.46385311j]]])
'''

#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
np.average(np.abs(rf_coil_line_matched.em_field.b_field[0,0,1,:])).round(10)

'''
Out:
    7.825e-07
'''

#Print the S11 parameter
rf_coil_line_matched.s_matrix.plotS(["S1-1"])
```
![](./docs/images/example_S.png)

**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](https://www.sciencedirect.com/science/article/pii/S0169260722000694).

## Test

For testing the library, [pytest](https://docs.pytest.org/en/stable/contents.html) is required.<br>
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 test_develop.py

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](https://tox.wiki/en/latest/) 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

## License

This project is licensed under the MIT
License - see the [LICENSE](LICENSE) file for
details.

## Related Publications

If you find CoSimPy useful for your work, please consider to cite this [paper](https://www.sciencedirect.com/science/article/pii/S0169260722000694)!


## Acknowledgments

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.

Maintenance of CoSimPy is made possible thanks to funds coming from the EPM STASIS project 21NRM05. The project (21NRM05 STASIS) has received funding from the European Partnership on Metrology, co-financed from the European Union's Horizon Europe Research and Innovation Programme and by the Participating States.

[![](./docs/images/EPM_logo.jpg)](https://metpart.eu/)
[![](./docs/images/MIMAS_logo.png)](https://www.ptb.de/mimas/home/)
[![](./docs/images/STASIS_logo.png)](https://www.ptb.de/stasis/)