|
| 1 | +--- |
| 2 | +title: PeakPerformance documentation |
| 3 | +--- |
| 4 | + |
| 5 | +# Welcome to the PeakPerformance documentation! |
| 6 | + |
| 7 | +[](https://pypi.org/project/peak-performance) |
| 8 | +[](https://github.com/JuBiotech/peak-performance) |
| 9 | +[](https://zenodo.org/doi/10.5281/zenodo.10255543) |
| 10 | + |
| 11 | + |
| 12 | +``peak_performance`` is a Python toolbox for Bayesian inference of peak areas. |
| 13 | + |
| 14 | +It defines PyMC models describing the intensity curves of chromatographic peaks. |
| 15 | + |
| 16 | +Using Bayesian inference, this enables the fitting of peaks, yielding uncertainty estimates for retention times, peak height, area and much more. |
| 17 | + |
| 18 | +# Installation |
| 19 | + |
| 20 | +```bash |
| 21 | +pip install peak-performance |
| 22 | +``` |
| 23 | + |
| 24 | +You can also download the latest version from [GitHub](https://github.com/JuBiotech/peak-performance). |
| 25 | + |
| 26 | + |
| 27 | +The documentation features various notebooks that demonstrate the usage. |
| 28 | + |
| 29 | +```{toctree} |
| 30 | +:caption: Tutorials |
| 31 | +:maxdepth: 1 |
| 32 | +
|
| 33 | +markdown/Installation |
| 34 | +markdown/Preparing_raw_data |
| 35 | +markdown/Peak_model_composition |
| 36 | +markdown/PeakPerformance_validation |
| 37 | +markdown/PeakPerformance_workflow |
| 38 | +markdown/Diagnostic_plots |
| 39 | +markdown/How_to_adapt_PeakPerformance_to_your_data |
| 40 | +``` |
| 41 | + |
| 42 | + |
| 43 | +```{toctree} |
| 44 | +:caption: Examples |
| 45 | +:maxdepth: 1 |
| 46 | +
|
| 47 | +notebooks/Ex1_Simple_Pipeline.ipynb |
| 48 | +notebooks/Ex2_Custom_Use_of_PeakPerformance.ipynb |
| 49 | +notebooks/Ex3_Pipeline_with_larger_example_dataset.ipynb |
| 50 | +``` |
| 51 | + |
| 52 | + |
| 53 | +In the following case studies we investigate certain aspects of peak modeling. |
| 54 | + |
| 55 | +```{toctree} |
| 56 | +:caption: Case Studies |
| 57 | +:maxdepth: 1 |
| 58 | +
|
| 59 | +notebooks/Investigation_doublepeak_separation.ipynb |
| 60 | +notebooks/Investigation_noise_sigma.ipynb |
| 61 | +``` |
| 62 | + |
| 63 | + |
| 64 | +Below you can find documentation that was automatically generated from docstrings. |
| 65 | + |
| 66 | +```{toctree} |
| 67 | +:caption: API Reference |
| 68 | +:maxdepth: 1 |
| 69 | +
|
| 70 | +pp_models |
| 71 | +pp_pipeline |
| 72 | +pp_plots |
| 73 | +``` |
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