cdi-stools 0.1.0

Coherent Diffraction Imaging Simulation Tools — BCDI simulation and NN-based phase retrieval

CDI-ST

Coherent Diffraction Imaging — Simulation Tools

A Python application for end-to-end Bragg Coherent Diffraction Imaging (BCDI): crystal lattice construction, X-ray scattering simulation, neural-network phase retrieval, and 3D visualization, in a single GUI.

Screenshots

Launcher	Material — crystal builder
Results — simulated Bragg peak	3D Viewer — reconstruction

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Installation

pip install cdi-stools

After installation, launch the GUI from any terminal:

cdi-st or cdi-stools

For ID01 / synchrotron .spec + .edf data support:

pip install "cdi-stools[id01]"

For everything:

pip install "cdi-stools[all]"

Support for other beamlines filenames comming in future updates...

Note: cdi-st ships a Qt-based GUI. On Linux, you may need system Qt dependencies (sudo apt install libxcb-cursor0 libxkbcommon-x11-0 on Ubuntu/Debian). On Windows and macOS the wheel includes everything.

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What is BCDI?

Bragg Coherent Diffraction Imaging is a synchrotron technique that reconstructs the 3D shape and atomic-displacement field of nanocrystals by inverting the diffraction intensity around a Bragg peak. The intensity gives only |F(q)|², so reconstruction must recover the missing phase by iterative algorithms (HIO, RAAR, ER), more recently by neural networks.

CDI-ST integrates the entire workflow into one tool:

Step	What you do	Where in the GUI
1. Build a crystal	Define material, shape, orientation, optional defects	BCDI Simulation → Material
2. Set the X-ray geometry	Beam energy, detector geometry, oversampling	BCDI Simulation → Beam
3. Simulate diffraction	Compute the 3D Bragg peak	BCDI Simulation → Results
4. Generate training data	Build a labeled dataset of (object, diffraction) pairs	BCDI Data Analysis → Generate Data
5. Train an unsupervised NN	AutoPhase_NN: physics-driven, no ground truth	BCDI Data Analysis → AutoPhase_NN
6. Train a supervised NN	CDI_NN: PhaseUNet3D learning the phase mapping	BCDI Data Analysis → CDI_NN
7. Reconstruct	NN → optionally refine with HIO/RAAR/ER	BCDI Data Analysis → Reconstruction
8. Visualize and export	Interactive 3D viewer, ParaView VTI export	BCDI Data Analysis → 3D Viewer

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The simulation pipeline

1. Material — build the crystal

Choose from preset materials (Si, Ge, Pt, Au, SiC, GaAs, ...) or import a custom CIF file. The crystal is built atom-by-atom from the unit cell, replicated to the requested supercell size.

You can:

• Select the particle shape: cube, sphere, cylinder, hexagonal prism, octahedron, dodecahedron
• Set the target oversampling ratio (controls how much of the diffraction volume the object occupies)
• Add strain fields: radial gradient, edge dislocation, random
• Add dislocations: edge / screw / mixed, at a configurable position with an arbitrary Burgers vector

The 3D lattice view (powered by Plotly) shows every atom in the supercell.

2. Beam — define the X-ray geometry

Select a reflection (h k l) from the auto-computed list of allowed peaks. The Beam tab also lets you set:

• Beam energy (and the resulting wavelength)
• Detector geometry: pixel pitch, sample-to-detector distance, detector size
• Beam size in microns
• Detector preset: Maxipix (516×516), Eiger 2M, custom rectangular

Detector preset auto-fills the geometry parameters.

3. Results — run the simulation

CDI-ST computes the diffraction in two paths:

• Analytical path (cube/rectangular shapes, no strain): closed-form Fourier transform of the support
• FFT path (any shape, any strain field): full 3D FFT of the complex object

Output: 3D diffraction volume |F(q)|², displayed as 2D slices (qx-qy, qx-qz) and in an interactive 3D Plotly viewer with iso-surface controls.

You can export the simulation as a .npz file ready for the data-analysis side, or for use as ground truth when training NNs.

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The data-analysis pipeline

4. Generate Data — create training samples

Generates a dataset of synthetic BCDI samples for NN training. Each sample contains the ground-truth phase, support, and the simulated diffraction intensity. You control:

• Number of samples (typically 2,000–10,000)
• Grid size (64³ recommended for training, 128³ for production inference)
• Materials (random or fixed)
• Particle size variation (random supercell multiplier)
• Random dislocations adds occasional edge/screw/mixed dislocations
• Random strain adds occasional radial/edge/random strain fields

Samples save as sample_XXXXX.npz and a manifest.json. Generation is parallelizable and resumable.

5. AutoPhase_NN — unsupervised physics-driven NN

AutoPhase_NN is a 3D dual-decoder convolutional network that learns to invert the Fourier modulus constraint without ground truth. It predicts the object's amplitude and phase such that |FFT(amp · exp(iφ))| matches the measured magnitude. The loss is a normalized-magnitude error plus optional regularizers on phase smoothness and support compactness. This is the network from (Yao, Yudong, et al. npj Computational Materials 8.1 (2022) ) reimplemented for this codebase.

Training is GPU-accelerated (CUDA preferred) and converges in a few dozen epochs on 2,000+ samples. The best checkpoint is saved automatically.

6. CDI_NN — supervised PhaseUNet3D

A 3D U-Net that predicts the phase field from the diffraction magnitude, trained against the ground-truth phase from the data generator.

Use AutoPhase_NN when you don't trust your simulator's ground truth (or have none). Use CDI_NN when you have a high-fidelity simulator and want a direct mapping. Use both via the ensemble mode in the Reconstruction tab.

7. Reconstruction — invert experimental or simulated data

Load any .npz (simulated) or .h5 (experimental) BCDI file, or convert a SPEC + EDF scan from ID01 directly via the dialog. Choose:

• NN-only: forward pass, twin-image suppression, COM centering
• Refined: NN seed → HIO + RAAR + ER iterative phase retrieval
• Ensemble: AutoPhase_NN + CDI_NN combined, optionally refined
• Compare: runs both modes side-by-side

The output is a complex object (amplitude + phase), a binary support, the R-factor convergence curve, and 2D slices at the COM of the reconstruction.

For experimental data, the loader handles:

• Detector chip-gap masking (Maxipix, Eiger)
• Hot-pixel removal
• Beamstop / direct-beam streak removal
• Bragg-peak-centered cropping
• Optional q-space orthogonalization via xrayutilities (with [id01] extra)

8. 3D Viewer — interactive visualization

The viewer renders the reconstruction in three modes:

• Point cloud: every voxel above threshold, colored by amplitude / phase / strain, with per-voxel opacity weighted by intensity
• Isosurface: smoothed iso-shell with internal data points showing through
• Surface only: bare iso-mesh with edges visible

Controls:

• Threshold (% of peak amplitude), voxel size, opacity, colormap
• Color scale auto / manual (vmin / vmax)
• Clip plane on any axis
• Mouse: left-drag rotate, right-drag pan, wheel zoom, double-click reset
• Export PNG snapshot or VTI (ParaView)

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Project structure

cdi-st/
├── src/cdi_st/
│   ├── __init__.py
│   ├── __main__.py              # python -m cdi_st
│   ├── bcdi_gui.py              # main entry point + launcher + splash
│   ├── bcdi_core.py             # crystal builder, simulator, reflection calculator
│   ├── nn_gui_tabs.py           # all NN-related GUI tabs
│   ├── nn_data_generator.py     # training-data generator
│   ├── nn_dataset.py            # PyTorch Dataset wrapper
│   ├── nn_phase_model.py        # PhaseUNet3D (supervised)
│   ├── nn_autophase_model.py    # AutoPhaseNet3D (unsupervised)
│   ├── nn_train.py              # supervised training CLI
│   ├── nn_autophase_train.py    # unsupervised training CLI
│   ├── nn_phase_retrieval.py    # HIO / RAAR / ER iterative algorithms
│   ├── nn_autophase_infer.py    # inference: nn_only / refined / ensemble
│   ├── nn_experimental_loader.py # h5/spec/edf loading + preprocessing
│   ├── nn_demo.py
│   ├── nn_visualize.py
│   └── CDI_ST_logo.png
├── tests/
├── docs/
├── pyproject.toml
├── README.md
├── LICENSE
└── CHANGELOG.md

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Citing

If you use CDI-ST in published research, please cite:

@software{saidi_cdi_st_2026,
  author       = {Saidi, Soufiane},
  title        = {CDI-ST: Coherent Diffraction Imaging — Simulation Tools},
  year         = {2026},
  publisher    = {GitHub},
  url          = {https://github.com/Refze/CDI-St}
}

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License

MIT — see LICENSE.

Author

Soufiane SAIDI — [e-mail]

We heavily encourage you to use the in-app Reports & Suggestions button to send any type of feedback.

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Acknowledgments

Built on the shoulders of the BCDI and python community: PyNX, BCDI-Utilities, cdiutils, xrayutilities, scikit-image, PyQt6, and the AutoPhaseNN paper by Yao et al. (2022).