Metadata-Version: 2.4
Name: pyMSFT
Version: 1.0.0
Summary: python MultiSlice Fourier-Transform toolbox
Author-email: Paul Tuemmler <paul.tuemmler@uni-rostock.de>, Christian Peltz <christian.peltz@uni-rostock.de>
License: CC-BY-4.0
Project-URL: Homepage, https://gitlab.uni-rostock.de/snp/pymsft
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: GPU :: NVIDIA CUDA
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Python: >=3.12
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy~=2.3.2
Requires-Dist: pyfftw~=0.15.0
Requires-Dist: tqdm~=4.67.1
Requires-Dist: scipy~=1.16.1
Requires-Dist: pillow~=11.3.0
Provides-Extra: mie
Requires-Dist: scattnlay~=2.4; extra == "mie"
Dynamic: license-file

      1.0.0      ___  ___ ___________ _____
      CC-BY-4.0  |  \/  |/  ___|  ___|_   _|
      _ __  _   _| .  . |\ `--.| |_    | |
     | '_ \| | | | |\/| | `--. \  _|   | |
     | |_) | |_| | |  | |/\__/ / |     | |
     | .__/ \__, \_|  |_/\____/\_|     \_/
     | |     __/ |        python Multi-Slice
     |_|    |___/         Fourier-Transforms

pyMSFT is a python toolbox for accurately and efficiently simulating wide-angle coherent diffractive imaging. 
For details on the physics implemented in this code, please refer to the following preprint: https://doi.org/10.48550/arXiv.2507.22483

## Installation
We strongly recommend setting up a virtual environment for the installation, i.e. using [conda](https://docs.conda.io/en/latest/).
This code has been tested for python 3.12.
```
git clone https://gitlab.uni-rostock.de/snp/pymsft.git
cd pymsft
conda create -n pymsft python=3.12
conda activate pymsft
```
At this point you can install the package using pip with different options:
- To install the basic package without GPU support and Mie scattering support, simply run:
    ```
    pip install .
    ```
- To take advantage of GPU acceleration we are using [CuPy](https://cupy.dev/). See the [installation instructions](https://docs.cupy.dev/en/stable/install.html) for more details. The simplest way to install it is currently via conda:
    ```
    conda install -c conda-forge cupy==13.6.0
    ``` 
    Note that an NVIDIA GPU with the corresponding driver installed is required.
- To calculate the Mie solutions for spherical particles you must also install corresponding Mie solver (we are using [scattnlay](https://github.com/ovidiopr/scattnlay)). This can be installed with:
    ```
    pip install .[mie]
    ```

## Usage
A simple example for a multislice simulation is shown below. Note that most of these examples require [matplotlib](https://matplotlib.org/) for visualization to be installed.

```python
import numpy as np
from pyMSFT.shapes import Spindle
from pyMSFT.simulation import PMSFTSimulation, SAXSSimulation

import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm

wavelength = 13.5e-9            # wavelength of the light in meters
Rsphere = 200e-9                # radius of the sphere in meters

refIndex = 0.89 + 1j * 0.09     # complex refractive index of Silver at 13.5 nm (92 eV)

npix_fft = 256  # number of pixels in the FFT (should be a power of 2)
npix_real = 64  # number of pixels in the real space simulation box - this will be zero padded to num_pix_fft internally

# setup the geometry and simulation
geometry = Spindle(refractive_index=refIndex, npix=npix_real, x_rotation=45 * np.pi / 180, y_rotation=30 * np.pi / 180, z_rotation=0)
simulation = PMSFTSimulation(npix_fft=npix_fft, npix_real=npix_real, wavelength=wavelength, box_size=2 * Rsphere, verbose=True)

# run the simulation and get the diffraction image
diff_image, (QX, QY) = simulation.get_diffraction_image(geometry, output_axes=True)

# visualize the result
_, axs = plt.subplots(1, 1, figsize=(6, 4))
q_extent = QX[0, 0], QX[-1, 0], QY[0, 0], QY[0, -1]
barn = 10 ** -28            # barn unit in m^2
diff_image /= barn ** 2     # convert to barn^2

img = axs.imshow(diff_image, extent=q_extent, cmap='turbo', norm=LogNorm(vmin=1e24, vmax=1e28))
plt.colorbar(img)
axs.set_title(r'differential cross section on Ewald sphere in [b$^2$]')

axs.set_xlabel('QX [1/m]')
axs.set_ylabel('QY [1/m]')
axs.set_aspect('equal')
plt.show()
```
This will create a diffraction pattern of a silver spindle illuminated with 13.5 nm light and should look like this:

![Diffraction pattern of a silver spindle](/docs/readme_example.png)

More examples can be found in the examples folder, e.g. for:
- Comparing different simulation methods with an exact Mie solution for spheres: `examples/compare_simulation_methods.py`

And more are planned and coming soon, e.g. for:
- Visualizing the experimental setup regarding the internal orientations and axes: `examples/visualize_setup.py`
- Comparing the performance of CPU and GPU simulations: `examples/compare_cpu_gpu.py`
- Compare different detector geometries: `examples/compare_detectors.py`
- Showcase the different available sample shapes: `examples/showcase_shapes.py`


## Citation
If you use this code for your research, please cite:
https://doi.org/10.48550/arXiv.2507.22483
