mirror of
https://github.com/c-sooyoung/bo-ptycho.git
synced 2026-09-17 17:19:08 +09:00
27 lines
1.0 KiB
Markdown
27 lines
1.0 KiB
Markdown
# bo-ptycho
|
|
|
|
This code is a (re)implementation of Bayesian Optimized ptychography in python for use at LEMON lab, used for
|
|
|
|
- Optimization-based Thickness Estimation and FIB-induced Damage Characterization of TEM Sample via Multislice Electron Ptychography (manuscript)
|
|
|
|
|
|
|
|
## Code usage
|
|
|
|
Prerequisites:
|
|
1. Create a python virtual environment with [requirements.txt](./requirements.txt)
|
|
2. Download [fold_slice](#)
|
|
2. Download [experimental data](#)
|
|
|
|
Reconstruction and parameter optimization:
|
|
1. Edit [config.yaml](./config.yaml)
|
|
- Example configurations can be found under `examples/silicon-fib/`
|
|
- Set io.input_data_path and io.result_dir as needed (WARNING: the result directory will be completely removed)
|
|
- Set ptycho.path to your fold_slice path
|
|
2. Edit [job.sub](./job.sub)
|
|
- Set the SBATCH configurations as needed (especially `gres` and `output`)
|
|
- The number of GPUs should match bo.batch in config.yaml
|
|
- Set the virtual environment path
|
|
- Set the config.yaml path
|
|
3. Submit the job via SLURM
|