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| 001 | 1045841 | ||
| 005 | 20251013170223.0 | ||
| 037 | _ | _ | |a FZJ-2025-03628 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Daniel, Davis Thomas |0 P:(DE-Juel1)185897 |b 0 |e Corresponding author |u fzj |
| 245 | _ | _ | |a ILTpy |
| 250 | _ | _ | |a 1.0.0 |
| 260 | _ | _ | |c 2025 |
| 336 | 7 | _ | |a Software |2 DCMI |
| 336 | 7 | _ | |a Software |b sware |m sware |0 PUB:(DE-HGF)33 |s 1760367644_10821 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a MISC |2 BibTeX |
| 336 | 7 | _ | |a Computer Program |0 6 |2 EndNote |
| 336 | 7 | _ | |a OTHER |2 ORCID |
| 336 | 7 | _ | |a Software |2 DataCite |
| 500 | _ | _ | |a Licensed under LGPL |
| 520 | _ | _ | |a ILTpy (/ɪltˈpaɪ/) is a python library for performing regularized inversion of one-dimensional or multi-dimensional data without non-negativity constraint. Contributions to respective distributions with both positive and negative sign are determined. Primary applications include magnetic resonance (NMR, EPR), and electrochemical impedance spectroscopy (distribution of relaxation times; DRT). ILTpy implements an inversion algorithm to fit experimental or simulated noisy data of complex materials by computing distributions of underlying physical or chemical properties. It was initially developed for magnetic resonance relaxation and diffusion data, and then also utilized for electrochemical impedance (EIS) data. These data often contain multiple components with varying distributions. Fitting a specific model, such as a mono-exponential, requires prior knowledge of the number of species present and yields only an effective characteristic constant. In contrast, inversion algorithms do not assume the shape or number of species in the system, but instead reveal the distribution of characteristic constants using a kernel suitable for modeling the response of a particular process. A common approach to analyzing magnetic resonance data using Inverse Laplace Transform (ILT) methods involves applying a non-negativity constraint to prevent oscillatory solutions. This constraint assumes that all relaxation components have the same sign. However, in systems where cross-relaxation or exchange occurs, such a constraint is unjustified, as it suppresses any relaxation components with negative values, potentially introducing artificial features in the resulting distributions that do not correspond to actual physical processes. In contrast, ILTpy avoids the use of a non-negativity constraint, employing instead a zero-crossing penalty along with uniform penalty regularization to stabilize the inversion process. |
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| 700 | 1 | _ | |a Bartsch, Christian Hippolyt |0 P:(DE-Juel1)187114 |b 1 |
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| 856 | 4 | _ | |u https://apps.fz-juelich.de/iltpy |
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| 980 | _ | _ | |a EDITORS |
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