Restructure documentation index

docs/source/index.md

@@ -0,0 +1,73 @@
+---
+title: PeakPerformance documentation
+---
+
+# Welcome to the PeakPerformance documentation!
+
+[![](https://img.shields.io/pypi/v/peak-performance)](https://pypi.org/project/peak-performance)
+[![](https://img.shields.io/badge/code%20on-Github-lightgrey)](https://github.com/JuBiotech/peak-performance)
+[![](https://zenodo.org/badge/DOI/10.5281/zenodo.10255543.svg)](https://zenodo.org/doi/10.5281/zenodo.10255543)
+
+
+``peak_performance`` is a Python toolbox for Bayesian inference of peak areas.
+
+It defines PyMC models describing the intensity curves of chromatographic peaks.
+
+Using Bayesian inference, this enables the fitting of peaks, yielding uncertainty estimates for retention times, peak height, area and much more.
+
+# Installation
+
+```bash
+pip install peak-performance
+```
+
+You can also download the latest version from [GitHub](https://github.com/JuBiotech/peak-performance).
+
+
+The documentation features various notebooks that demonstrate the usage.
+
+```{toctree}
+:caption: Tutorials
+:maxdepth: 1
+
+markdown/Installation
+markdown/Preparing_raw_data
+markdown/Peak_model_composition
+markdown/PeakPerformance_validation
+markdown/PeakPerformance_workflow
+markdown/Diagnostic_plots
+markdown/How_to_adapt_PeakPerformance_to_your_data
+```
+
+
+```{toctree}
+:caption: Examples
+:maxdepth: 1
+
+notebooks/Ex1_Simple_Pipeline.ipynb
+notebooks/Ex2_Custom_Use_of_PeakPerformance.ipynb
+notebooks/Ex3_Pipeline_with_larger_example_dataset.ipynb
+```
+
+
+In the following case studies we investigate certain aspects of peak modeling.
+
+```{toctree}
+:caption: Case Studies
+:maxdepth: 1
+
+notebooks/Investigation_doublepeak_separation.ipynb
+notebooks/Investigation_noise_sigma.ipynb
+```
+
+
+Below you can find documentation that was automatically generated from docstrings.
+
+```{toctree}
+:caption: API Reference
+:maxdepth: 1
+
+pp_models
+pp_pipeline
+pp_plots
+```

docs/source/notebooks/Ex1_Simple_Pipeline.ipynb

@@ -192,7 +192,7 @@
     "These objects contain not only the timeseries of the particular signal but also samples from the prior predictive, posterior, and posterior predictive sampling.  \n",
     "This allows you to explore the data in detail and/or build your own plots aside from the ones featured in PeakPerformance.  \n",
     "  \n",
-    "It is highly recommended to check the documentations for [`PyMC`](docs.pymc.io/) and [`ArviZ`](https://python.arviz.org/en/latest/) to get information and inspiration for this purpose."
+    "It is highly recommended to check the documentations for [`PyMC`](https://docs.pymc.io/) and [`ArviZ`](https://python.arviz.org/en/latest/) to get information and inspiration for this purpose."
    ]
   },
   {