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light weight code for solving the electrical impedance tomography problem via PDE constrained optimization

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Solving EIT via PDE constrained optimization

This is a python implementation of a optimization-based estimation of the EIT problem, using PDE constrained optimization, without regularization.

Requirements:

-numpy (with MKL backend)

-scipy

-numba

-pypardiso

For some of the examples the scipy.io library to extract the data (already contained in the tests folder)

Installation

To install just go to the main directory and install as usual:

python setup.py install

This will install the package on your local python environment (we strongly recommend to create a virtual environment before hand)

Tests

We have added a context.py script in the test folder, this would allow to run the test files without having to install the package on your local environment.

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light weight code for solving the electrical impedance tomography problem via PDE constrained optimization

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