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Orochi PDFget: 1D Total Scattering Reduction & Faber–Ziman Normalization

The orochi.pdfget package provides rigorous 1D total scattering reduction, transforming raw experimental scattering profiles I(2θ) or I(Q) into the total structure factor S(Q), reduced structure factor F(Q) and atomic pair distribution function G(r) using the Faber–Ziman formalism.

Key Features

  • Background & Solvent Halo Subtraction: Performs scaling and subtraction of empty container, air, or liquid solvent scattering.

  • Compton Recoil & Fluorescence Correction: Subtracts incoherent Compton inelastic scattering and flat fluorescence baselines.

  • High-Q Form-Factor Normalization: Normalizes experimental intensity against theoretical atomic scattering <|f(Q)|^2> + I_Compton(Q) over a user-defined high-Q polynomial window [Q_fit,min, Q_fit,max].

  • Fourier Transformation via DST-I: Implements the Type-I Discrete Sine Transform (DST-I) with optional Lorch modification functions to minimize termination ripples without introducing phase shifts.

Quickstart: 1D Reduction to G(r)

from orochi.pdfget import PDFget
import matplotlib.pyplot as plt

# 1. Initialize PDFget
pdfget = PDFget()
pdfget.read_file("ceo2_1d_profile.txt", target="sample")

# 2. Configure sample chemistry and reduction interval
pdfget["composition"] = "CeO2"
pdfget["rambda"] = 0.18033           # Incident wavelength in Angstrom
pdfget["qmin"] = 1.0                 # Integration bounds
pdfget["qmax"] = 25.0
pdfget["fitmin"] = 15.0              # Normalization window
pdfget["fitmax"] = 25.0
pdfget["rmin"] = 0.0
pdfget["rmax"] = 30.0

# 3. Execute 1D reduction pipeline
pdfget.run()

# 4. Access reduced structure factor F(Q) and G(r)
q_grid = pdfget.q
fq_data = pdfget.fq

r_grid = pdfget.r
gr_data = pdfget.gr

plt.plot(r_grid, gr_data)
plt.xlabel("r (Å)")
plt.ylabel("G(r) (Å⁻²)")
plt.show()

Citation

If you use orochi.pdfget in a scientific publication, please cite this package as:

S. Tominaka, Orochi: A Physics-Grounded Python Package for Pair Distribution Function Analysis of Crystalline and Low-Dimensional Materials, 2026. https://github.com/Tominaka-Laboratory/orochi.pdfget

Installation

The preferred method is to install using pip:

pip install orochi.pdfget

To confirm that the installation was successful, type:

python -c "import orochi.pdfget; print('Orochi PDFget imported successfully')"

If you prefer to install from source:

git clone https://github.com/Tominaka-Laboratory/orochi.pdfget.git
cd orochi.pdfget
pip install -e .

Getting Started

You may consult our online documentation for tutorials and API references.

Support and Contribute

The orochi.pdfget project welcomes your expertise and enthusiasm!

If you see a bug or want to request a feature, please report it as an issue and/or submit a fix as a PR.

Feel free to fork the project and contribute. To install orochi.pdfget in a development mode, with its sources being directly used by Python rather than copied to a package directory, use the following in the root directory

pip install -e .

To ensure code quality and to prevent accidental commits into the default branch, please set up the use of our pre-commit hooks.

Improvements and fixes are always appreciated.

Before contributing, please read our Code of Conduct.

License

This project is licensed under the GNU General Public License v3.0 (GPLv3). See the LICENSE.rst file for details.

This copyleft license guarantees that Orochi remains freely accessible, open, and transparent to the entire scientific community, and that all derivative works must likewise remain open source.

Citation

If you use this package in academic research, please cite:

@article{tominaka2026orochi,
  title   = {Orochi: A Physics-Grounded Python Package for Pair Distribution Function Analysis of Crystalline and Low-Dimensional Materials},
  author  = {Tominaka, Satoshi},
  year    = {2026},
  url     = {https://github.com/Tominaka-Laboratory/orochi}
}

Contact

For more information on orochi.pdfget please visit the project web-page or email Dr. Satoshi Tominaka at tominaka.satoshi@nims.go.jp.

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