pyleem 0.3.0

Low Energy Electron Microscopy data analysis toolkit

pyLEEM

A Python toolkit for Low Energy Electron Microscopy (LEEM) data analysis. pyLEEM provides readers, ROI tools, domain-specific analyzers, calibration analyzers, and workflow recording for reproducible runs.

Installation

pip install pyleem

Development installation

Clone the development branch:

git clone --branch develop https://github.com/peterhys/PyLEEM.git
cd PyLEEM
pip install -e .

Design

Overview

pyLEEM is a Python toolkit for analyzing Low Energy Electron Microscopy (LEEM) data. It provides tools for reading LEEM metadata and raw data, and for calibrating and analyzing X-ray Photoelectron Spectroscopy (XPS), Secondary Electron Energy Spectroscopy (SEES), Diffuse Elastic Scattering Pattern (DESP), and X-ray Absorption Spectroscopy (XAS).

Additional tools include stitching that can create a continuous spectrum from overlapping spectra.

The package is designed to be modular and extendable.

Reader class

The Reader class takes care of converting a LEEM file into a Python-readable format, and parses all the metadata from the file. Currently, the only available reader is the UViewReader class, used to read UView .dat files. Custom readers can be easily implemented to support other file formats.

ROI class

The ROI class is used to define the region of interest (ROI) for the analysis. ROI can either be manually defined or parsed from an ImageJ ROI file. Calibrated ROI stores calibration parameters for the region of interest. For LEEM, the pixel per eV and peak shift values are used to convert the pixel profile to the proper energy scale.

Analyzer class

The base unit of pyLEEM analysis is an Analyzer object. The basic Analyzer class provides simple analysis tools for raw data.

For domain-specific analysis, Analyzer subclasses can be created. Currently, the available subclasses are:

• SEESAnalyzer: Secondary Electron Energy Spectroscopy (SEES) analyzer.
• DESPAnalyzer: Diffuse Elastic Scattering Pattern (DESP) analyzer.
• XASAnalyzer: X-ray Absorption Spectroscopy (XAS) analyzer.
• XPSAnalyzer: X-ray Photoelectron Spectroscopy (XPS) analyzer.

These subclasses provide domain-specific analysis tools for the raw data.

Workflow

For reproducibility, workflow can be used to build readers, ROI, and analyzer objects, and define tasks. Workflow can be loaded and saved from configuration TOML files.

Users can instantiate readers, ROI, and analyzers directly without Config or Workflow. Users use Workflow when they want standardized calibration, reproducible input, parameters, and streamlined I/O.

Examples

For detailed examples, please refer to the example notebook.

The notebook uses the external example data repository. Clone it as example/ next to the notebook:

git clone https://github.com/peterhys/pyLEEM-example.git example

Here we show some examples of pyLEEM's capabilities.

XPS: binding energy calibration, peak fitting and stitching

from pyleem.analysis.xps import XPSAnalyzer, XPSCalibration
from pyleem.reader import UViewReader, read_files
from pyleem.roi import LineROI

readers = read_files(
    ["xps_0.dat", "xps_1.dat", "xps_2.dat"],
    UViewReader,
    metadata_list=[
        {"Beam Energy": (400, "eV")},
        {"Beam Energy": (400, "eV")},
        {"Beam Energy": (400, "eV")},
    ],
)

roi = LineROI(src=[0, 0], dst=[0, 127], linewidth=1)

calibration = XPSCalibration(readers, roi)
cal_result = calibration.analyze(
    baselines=[(197, 100)] * 3,
    num_peaks=1,
    ref_index=0,
    ref_value=285.0,
)

analyzer = XPSAnalyzer(
    readers,
    roi,
    cal_result["pixel_per_ev"],
    cal_result["peak_shift"],
)

fit_result = analyzer.fit(0, num_peaks=1, baseline=(200, 100))

# Automatically stitch profiles into a single spectrum
stitched_energy, stitched_profile = analyzer.stitch_profiles([0, 1, 2])

SEES: surface potential from an onset shift

from pyleem.analysis.sees import SEESAnalyzer, SEESCalibration
from pyleem.reader import UViewReader, read_files
from pyleem.roi import LineROI

readers = read_files(["sees_0.dat", "sees_1.dat", "sees_2.dat"], UViewReader)
roi = LineROI(src=[0, 0], dst=[0, 127], linewidth=1)

calibration = SEESCalibration(readers, roi)
cal_result = calibration.analyze(sigma=0)

analyzer = SEESAnalyzer(
    [readers[0]],
    roi,
    cal_result["pixel_per_ev"],
    cal_result["peak_shift"],
    sigma=10,
)

result = analyzer.analyze_profile(0)
print(result["surface_potential"])

Workflow: run and save analysis results

Workflow file can be partially filled as a template for distribution.

sees_calibration.toml

[session]
reader = "UViewReader"
roi = "LineROI"
analyzer = "SEESCalibration"

[reader]
paths = ["sees_0.dat", "sees_1.dat", "sees_2.dat"]

[roi]
src = [0, 0]
dst = [0, 127]
linewidth = 1

[task]
sigma = 0

import pyleem.analysis.sees  # registers SEESCalibration class
from pyleem.config import load_config
from pyleem.workflow import Workflow

config = load_config("sees_calibration.toml")
workflow = Workflow(config)
result = workflow.run()
workflow.save("sees_calibration_run.toml")

Documentation

• Documentation
• Changelog
• Contributing

License

pyLEEM is distributed under the BSD 3-Clause License, see LICENSE.

Additional Brookhaven National Laboratory, U.S. Department of Energy, and U.S. Government rights notices are provided in NOTICE.

Third-party dependencies are distributed under their own licenses, see THIRD_PARTY_LICENSES.