From e917b929916856cecd26c45c95ea5d40183753cb Mon Sep 17 00:00:00 2001 From: Michael Osthege Date: Sun, 13 Oct 2024 15:21:12 +0200 Subject: [PATCH 1/2] Add https:// to links --- docs/source/notebooks/Ex1_Simple_Pipeline.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/notebooks/Ex1_Simple_Pipeline.ipynb b/docs/source/notebooks/Ex1_Simple_Pipeline.ipynb index 240f369..4234579 100644 --- a/docs/source/notebooks/Ex1_Simple_Pipeline.ipynb +++ b/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." ] }, { From 4339aedc7bf070dbf5487f97370a1cc875ea9216 Mon Sep 17 00:00:00 2001 From: Michael Osthege Date: Sun, 13 Oct 2024 15:21:19 +0200 Subject: [PATCH 2/2] Restructure documentation index Switched from RestructuredText to MyST because it's easier. Created sections in the left-hand sidebar as discussed. --- docs/source/index.md | 73 +++++++++++++++++++++++++++++++++++++++++++ docs/source/index.rst | 61 ------------------------------------ 2 files changed, 73 insertions(+), 61 deletions(-) create mode 100644 docs/source/index.md delete mode 100644 docs/source/index.rst diff --git a/docs/source/index.md b/docs/source/index.md new file mode 100644 index 0000000..f1839b4 --- /dev/null +++ b/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 +``` diff --git a/docs/source/index.rst b/docs/source/index.rst deleted file mode 100644 index 89ffaeb..0000000 --- a/docs/source/index.rst +++ /dev/null @@ -1,61 +0,0 @@ -Welcome to the PeakPerformance documentation! -============================================= - -.. image:: https://img.shields.io/pypi/v/peak-performance - :target: https://pypi.org/project/peak-performance - -.. image:: https://img.shields.io/badge/code%20on-Github-lightgrey - :target: https://github.com/JuBiotech/peak-performance - -.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.10255543.svg - :target: 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 -============ - -.. code-block:: bash - - pip install peak-performance - -You can also download the latest version from `GitHub `_. - -Tutorials -========= - -The documentation features various notebooks that demonstrate the usage and investigate certain aspects of peak modeling. - -.. toctree:: - :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 - notebooks/Ex1_Simple_Pipeline.ipynb - notebooks/Ex2_Custom_Use_of_PeakPerformance.ipynb - notebooks/Ex3_Pipeline_with_larger_example_dataset.ipynb - notebooks/Investigation_doublepeak_separation.ipynb - notebooks/Investigation_noise_sigma.ipynb - notebooks/Processing_test_1_raw_data.ipynb - notebooks/Create_validation_plot_from_raw_data.ipynb - - -API Reference -============= - -.. toctree:: - :maxdepth: 2 - - pp_models - pp_pipeline - pp_plots