darling-pypi 1.0.0

A python [D]ark field x-ray microscopy [A]nalysis & [R]econstruction [L]ibrary for rapid data [IN]spection & [G]raphing

DARLING

the python [D]ark field x-ray microscopy [A]nalysis & [R]econstruction [L]ibrary for rapid data [IN]spection & [G]raphing

Authors

darling is written and maintained by:

Axel Henningsson, Felix Tristan Frankus and Adam André William Cretton

affiliated with DTU. The core ideas of this library was originally written during a beamtime at ESRF id03D.

Until an associated journal publication is available, if you use this code in your research, we ask that you cite this repository.

If you are interested in collaborating with us on DFXM data analysis, please reach out to us at: [e-mail] and we can discuss the possibilities.

Usecase

Loading & plotting Data

Darling collects many useful tools in the properties module.

For example, it is possible to model the angular intensity distribution of a scan using a Gaussian Mixture Model (GMM) and then color code the scan based on the first maxima of the GMM using a RGB color map known as a mosaicity map.

import numpy as np

# read some toy data in
path_to_data, _, _ = darling.assets.domains()  # replace with your own data
scan_id = "1.1"  # tope level key in the hdf5 file
dset = darling.DataSet(path_to_data, scan_id)

# model the intensity distribution using a GMM
features = darling.properties.gaussian_mixture(dset.data, k=4, coordinates=dset.motors)

# color code the scan based on the first maxima of the GMM
first_maxima_mean = np.concatenate(
   (features["mean_motor1"][..., 0, None], features["mean_motor2"][..., 0, None]),
   axis=-1,
)
rgbmap, colorkey, colorgrid = darling.properties.rgb(
   first_maxima_mean, norm="dynamic", coordinates=dset.motors
)

To visualize the resulting mosaicity map, you can use the following code:

import matplotlib.pyplot as plt

# plot the resulting mosaicity map
plt.style.use('dark_background')
fig, ax = plt.subplots(1, 1, figsize=(7,7))
im = ax.imshow(rgbmap)
plt.tight_layout()
plt.show()

for more examples see the externally hosted documentation at https://axelhenningsson.github.io/darling/

Installation

From source the key is simply to clone and pip install

git clone https://github.com/AxelHenningsson/darling.git
cd darling
pip install -e .

In general, you probably want to install in a fresh virtual environment as

python3 -m venv .venv_darling
source .venv_darling/bin/activate
git clone https://github.com/AxelHenningsson/darling.git
cd darling
pip install -e .

use

source .venv_darling/bin/activate

whenever you want to activate the environment. To add your env into a jupyter kernel such that you can use it in an interactive notebook you may add the following two commands:

pip install ipykernel
python -m ipykernel install --user --name=darling

Note on jupyter & the ESRF slurm cluster

In the main ESRF slurm Python jupyter kernel it is possible to do the following hack to get the latest darling running.

git clone https://github.com/AxelHenningsson/darling.git
sys.path.insert(0, os.path.abspath('./darling'))
import darling

This trick is possible since that all dependencies of darling are already installed in the big Python jupyter kernel at ESRF.

The following snippet has also been verified to work on the ESRF slurm cluster 19 Dec 2024 in a browser terminal:

python3 -m venv .venv_darling
source .venv_darling/bin/activate
git clone https://github.com/AxelHenningsson/darling.git
cd darling
pip install -e .
pip install ipykernel
python -m ipykernel install --user --name=darling

This appraoch should work on other clusters as well, as long as some user permission to install exists.

Documentation

Darling hosts documentation at https://axelhenningsson.github.io/darling/