orochi.pixia
Orochi PIXIA: 2D Area Detector Reduction & Geometry Calibration
The orochi.pixia package provides advanced 2D area detector data reduction, instrumental geometry calibration, and azimuthal integration tailored for high-energy synchrotron total scattering. Operating on raw detector images (flat panels, TIFF, EDF), orochi.pixia extracts pristine 1D diffraction profiles I(2θ) and I(Q) free from oblique sensor penetration distortion and edge-truncation artifacts.
Key Features
Detector Geometry Calibration: Refines incident beam center (x₀, y₀), calibrated sample-to-detector distance L₀, and detector tilt/rotation angles against standard calibration materials (SRM CeO₂, Ni).
Physical Sensor Corrections: Corrects for beam polarization, solid-angle distortion, detector thickness oblique absorption (μ_det · t_det), and capillary sample attenuation.
Automated Beamstop & Defect Masking: Automatically identifies circular beamstop shadows, inter-module gaps, and dead pixels.
Diagonal Sector Integration: Integrates over designated diagonal corner lobes (β = ±45° ± 18°, ±135° ± 18°), avoiding orthogonal detector edge boundaries to access maximum momentum transfer Q_max with invariant angular acceptance.
Quickstart: 2D Image Reduction
from orochi.pixia import Pixia
import matplotlib.pyplot as plt
# 1. Initialize Pixia
pixia = Pixia()
pixia.read_file("raw_detector_pattern.tif", target="sample")
# 2. Configure calibrated beamline geometry
pixia["beam_center_x"] = 1025.60
pixia["beam_center_y"] = 1053.13
pixia["distance"] = 227.575 # Sample-to-detector distance in mm
pixia["rambda"] = 0.18033 # Wavelength in Angstrom
pixia["pixel_size_x"] = 0.20 # Pixel pitch in mm
pixia["pixel_size_y"] = 0.20
# 3. Configure sector integration
pixia["integration_mode"] = "sector" # 'full' (360 deg) or 'sector' (diagonal lobes)
pixia["sector_angles"] = [-135, -45, 45, 135]
pixia["sector_delta"] = 18.0
# 4. Execute reduction pipeline
pixia.run()
# 5. Extract reduced 1D profile
two_theta = pixia.tth
intensity = pixia.intensity
plt.plot(two_theta, intensity)
plt.xlabel("2θ (deg)")
plt.ylabel("Corrected Intensity (counts)")
plt.show()
Citation
If you use orochi.pixia 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.pixia
Installation
The preferred method is to install using pip:
pip install orochi.pixia
To confirm that the installation was successful, type:
python -c "import orochi.pixia; print('Orochi PIXIA imported successfully')"
If you prefer to install from source:
git clone https://github.com/Tominaka-Laboratory/orochi.pixia.git
cd orochi.pixia
pip install -e .
Getting Started
You may consult our online documentation for tutorials and API references.
Support and Contribute
The orochi.pixia 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.pixia 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.pixia please visit the project web-page or email Dr. Satoshi Tominaka at tominaka.satoshi@nims.go.jp.
Metadata
Release files for orochi.pixia 4.0.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| orochi_pixia-4.0.0.tar.gz | 62.5 kB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| orochi_pixia-4.0.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 126.9 kB
Release files / orochi_pixia-4.0.0.tar.gz
| Download URL | orochi_pixia-4.0.0.tar.gz |
|---|---|
| Size | 62.5 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
33de50cc7e6b054012589893ff5b344e752f9ced32aeefe2257d00cffe484516
|
|
BLAKE2b-256 checksum How to use checksums |
b8ab4663c4d9a8454bc78be9dce59a129de3c8d5b972543a434ddbfb27f04db7
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.11.17
|
Release files / orochi_pixia-4.0.0-py3-none-any.whl
| Download URL | orochi_pixia-4.0.0-py3-none-any.whl |
|---|---|
| Size | 64.4 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
2576a03fb54dfd531b7cadffbb8554bc965a4c67fefe27dbc4f0d539f7004cd0
|
|
BLAKE2b-256 checksum How to use checksums |
46b2c53ab84d166b95fd1edb4c77f9ef1feaa3df2028733160f5b441ac871f64
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.11.17
|
Security for orochi.pixia 4.0.0
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.