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docs/source/index.md

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---
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title: PeakPerformance documentation
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---
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# Welcome to the PeakPerformance documentation!
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[![](https://img.shields.io/pypi/v/peak-performance)](https://pypi.org/project/peak-performance)
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[![](https://img.shields.io/badge/code%20on-Github-lightgrey)](https://github.com/JuBiotech/peak-performance)
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[![](https://zenodo.org/badge/DOI/10.5281/zenodo.10255543.svg)](https://zenodo.org/doi/10.5281/zenodo.10255543)
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``peak_performance`` is a Python toolbox for Bayesian inference of peak areas.
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It defines PyMC models describing the intensity curves of chromatographic peaks.
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Using Bayesian inference, this enables the fitting of peaks, yielding uncertainty estimates for retention times, peak height, area and much more.
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# Installation
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```bash
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pip install peak-performance
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```
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You can also download the latest version from [GitHub](https://github.com/JuBiotech/peak-performance).
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The documentation features various notebooks that demonstrate the usage.
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```{toctree}
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:caption: Tutorials
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:maxdepth: 1
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markdown/Installation
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markdown/Preparing_raw_data
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markdown/Peak_model_composition
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markdown/PeakPerformance_validation
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markdown/PeakPerformance_workflow
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markdown/Diagnostic_plots
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markdown/How_to_adapt_PeakPerformance_to_your_data
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```
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```{toctree}
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:caption: Examples
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:maxdepth: 1
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notebooks/Ex1_Simple_Pipeline.ipynb
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notebooks/Ex2_Custom_Use_of_PeakPerformance.ipynb
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notebooks/Ex3_Pipeline_with_larger_example_dataset.ipynb
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```
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In the following case studies we investigate certain aspects of peak modeling.
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```{toctree}
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:caption: Case Studies
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:maxdepth: 1
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notebooks/Investigation_doublepeak_separation.ipynb
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notebooks/Investigation_noise_sigma.ipynb
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```
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Below you can find documentation that was automatically generated from docstrings.
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```{toctree}
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:caption: API Reference
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:maxdepth: 1
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pp_models
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pp_pipeline
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pp_plots
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```

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