EEmiLib 0.1.5

Fit various Electron EMIssion models on experimental data.

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

1. Create a dedicated Python environment.
2. 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:

1. Install Python 3.12 or higher.
2. Learn to use Python environments , pipenv or virtualenv.
3. Optionally, install a Python IDE such as Spyder or VSCode.

For developers

If you want to edit the source code:

1. Clone the repository: git clone [e-mail]:AdrienPlacais/EEmiLib.git
2. Create a dedicated Python environment.
3. From EEmiLib folder: pip install -e .[test]
4. 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.

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,
)

Roadmap/To-Do

• Document abbreviations

• GUI:

  • Better handling of multiple Plot data and Plot model buttons push.
  • Display quantitative criteria to assess model quality (e.g., Nicolas Fil's criterion)
  • Display derived quantities such as crossover energies, maximum TEEY, etc.

• CI:

  • PyPI release.
  • Update installation instructions.
  • ? Allow execution on online Docker, or make executable?

• Export buttons

  • Tabulated model data.
  • Model parameters value (makes sense along with an Import button).

• Fix error when not running from a git repo:

  fatal: not a git repository (or any parent up to mount point /)
  Stopping at filesystem boundary (GIT_DISCOVERY_ACROSS_FILESYSTEM not set).`
  

• API:

  • Model.display_parameters() method for nice printing.
  • Import/export model configuration with a TOML.

• If it proves useful:

  • Handle experimental data with error bars
  • Add control over interpolation of loaded experimental data
  • Optional smoothing of measured data
  • Different line styles/colors for different populations.
  • Ready-to-use interface for PIC codes.