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monier2020fast codes

Fast reconstruction of atomic-scale STEM-EELS images from sparse sampling.

These codes aim at reproducing the paper results.


The codes rely on the pystem python library (version 0.1.1) that I developped for this purpose.

The code has been tested on python 3.6 only.

Using the requirement file

A requirement file is provided to install a new environment. My advice is to use pyenv coupled with pyenv-virtualenv to create a virtual environment. Then, simply setup the environment with

pip install -r requirements.txt

Manual installation

Another way is to manually install the requirements.

pip install scikit-image==0.16.2 hyperspy==1.5.2 inpystem==0.1.1 PyQt5 inquirer blessings

Warning: The inpystem version is 0.1.1. The scikit and hyperspy versions are set as a conflict was discovered recently discovered (see here).

Getting the images

The images disk usage is 327.6 Mo, so that it is not hosted by github. To get them, please follow these steps:

  1. Download the data here,
  2. Unzip it into the codes directory.
  3. Ensure the acquisitions/ folder is loacated at the same level as the python codes.

For linux users, this should help.

cd /path/to/the/codes


To reproduce the results, one should first build the reconstructed data to generate the figures afterwards.

Build data

To build the data, please execute the program:

$ python

The data to process and the reconstruction methods to execute will be asked in the console. Be aware that this is highly time-consuming, expecially for R1 and for dictionary-learning methods. I recommand you to first try the R2 and S data for some methods and add other configuration afterwards. All output data are stored in the reconstructionfolder.

Generate results

To generate the results, the python interpreter should be interactive. We then recommend you to use ipython instead of python alone. Just type:

$ ipython 

Python 3.6.2 (default, Jul 24 2019, 11:45:48) 
Type 'copyright', 'credits' or 'license' for more information
IPython 7.16.1 -- An enhanced Interactive Python. Type '?' for help.

In [1]: %run

Again, the program will ask you which figure you are interested in. Finally, it will propose you to display the results, to save the results or to perform both. The results are saved in the results directory.

Author and license

These codes were written by Etienne Monier and are distributed under the MIT license.


Etienne Monier, Thomas Oberlin, Nathalie Brun, Xiaoyan Li, Marcel Tencé, Nicolas Dobigeon - Fast reconstruction of atomic-scale STEM-EELS images from sparse sampling.




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