This is code discussed in Brunmair, Gotsmy et al. 2021 [1] 
Python 3.7 and packages listed in requirements.txt.
pip install -r requirements.txt
Here we fitted our model onto raw time-series data.
The notebook mathematical_model/donor_fitting.ipynb is used to fit kinetic/ concentration/ Vsweat parameters of our model onto the measured (IS normalized) data (M̃) given in mathematical_model/raw_data/.
Custom python functions are listed in the notebook in section functions. In sampling for MC replicates & multiprocess fitting the actual fitting is done and the results are saved in the mathematical_model/fitted_parameters/ directory. In sections parse results and plot results best solution for every time-series is read in and the unit-less concentrations of caffeine, paraxanthine, theobromine, and theophylline (as described in the Supplementary Notes: Mathematical Model) are plotted.
The notebook mathematical_model/donor_analysis.ipynb provides code that has been used to create figures shown in the manuscript and supplementary material. There we used donor 1 and donor 2 as examples, here the figures are plotted for all donors.
Here we analysed the error associated to the fitting procedure with theoretical simulations.
To run a test simulation go to sensitivity_analysis/test and run the run_simulation.py script. Note: with standard settings this can take several hours, depending on your machine. To run a shorter test consider lowering the values for n and i (max_repsand max_tries respectively).
cd sensitivity_analysis/test
python ../run_simulation.py
This will create a new run_simulation.txt file with information about original kinetic & concentration parameters/ timepoints/ Vsweat and error values as well as the best solution parameters per bootstrap replicate. Optionally, it will create a run_simulation_raw directory with i files that store all n·(11+j) fitting parameters and n losses.
The table run_information.csv gives an overview of the runs simulated for the data shown in the manuscript and their settings and names. The raw data of the runs is given in sensitivity_analysis/runs_manuscript/. The notebook sensitivity_analysis/sensitivity_analysis.ipynb shows how the data was processed to extract CV and MRE of the different simulations. The resulting tables are found in the sensitivity_analysis/analysis_tables/ directory. They correspond to the tables shown in the Supplementary Notes: Sensitivity Analysis section of the manuscript.
The code (and especially the implemented loss functions) heavily relies on the scipy[2] and robust_loss_pytorch[3] packages with corresponding licenses found here and here repsectively. Original code is licensed under GPL v3 (see LICENSE file).
This code can be cited using following DOI 
[1] Brunmair, J., Gotsmy, M., Niederstaetter, L., Neuditschko, B., Bileck, A., Slany, A., Feuerstein, M.L., Langbauer, C., Janker, L., Zanghellini, J., Meier-Menches, S.M., & Gerner, C. (2020). Finger sweat analysis enables short interval metabolic biomonitoring in humans. 
[2] Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., ... & van Mulbregt, P. (2020). SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature methods, 17(3), 261-272.
[3] Barron, J. T. (2019). A general and adaptive robust loss function. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 4331-4339).