Welcome to EEmiLib’s documentation!

Contents

EEmiLib

EEmiLib (Electron EMIssion Library) provides several electron emission models and a simple interface to fit them to experimental data. It includes both a graphical user interface (GUI) for ease of use and a Python API for advanced users.

The library focuses on electron emission models relevant to multipactor simulations, i.e. for impinging energies ranging from a few eV to several hundred eV.

This project is still under active development. I maintain it in my free time, but I’ll do my best to answer any questions you may have.

Features

Multiple electron emission models
Graphical interface for quick model fitting
Python API for scripting and automation

Installation

For users

Create a dedicated Python environment.
Run

pip install EEmiLib

[!NOTE] If you are completely new to Python and these instructions are unclear, check this tutorial. In particular, you will want to:

Install Python 3.12 or higher.

Learn to use Python environments , pipenv or virtualenv.

Optionally, install a Python IDE such as Spyder or VSCode.

For developers

If you want to edit the source code:

Clone the repository: git clone git@github.com:AdrienPlacais/EEmiLib.git
Create a dedicated Python environment.
From EEmiLib folder: pip install -e .[test]
Test that everything is working with pytest -m "not implementation".

[!WARNING] If you Download ZIP this repository (which can happen if you don’t have access to git), installation will fail at step #3. A workaround is proposed here. This is a different library, but the same method applies.

Usage

Graphical User Interface

To start the GUI, run following command in a bash:

eemilib-gui

A Module not found error generally means that the EEmiLib Python environment is not activated.

GUI animation

Python API

import numpy as np
from eemilib.emission_data import DataMatrix
from eemilib.loader import PandasLoader
from eemilib.model import Vaughan
from eemilib.plotter import PandasPlotter
from eemilib import teey_cu


filepath = [teey_cu / "measured_TEEY_Cu_1_eroded.csv"]

# Object holding filepaths
data_matrix = DataMatrix()
# Indicate that this is TEEY file
data_matrix.set_files(
    filepath, population="all", emission_data_type="Emission Yield"
)
data_matrix.load_data(PandasLoader())

# Plot experimental data
plotter = PandasPlotter()
axes = data_matrix.plot(
    plotter, population="all", emission_data_type="Emission Yield"
)

# Select model and fit
model = Vaughan()
model.find_optimal_parameters(data_matrix)

# Plot fitted data
model.plot(
    plotter,
    population="all",
    emission_data_type="Emission Yield",
    energies=np.linspace(0, 1000, 1001),
    angles=np.linspace(0, 60, 4),
    axes=axes,
)