Update landing page

README.md

@@ -4,33 +4,14 @@
 [![documentation](https://readthedocs.org/projects/peak-performance/badge/?version=latest)](https://peak-performance.readthedocs.io/en/latest)
 [![DOI](https://zenodo.org/badge/713469041.svg)](https://zenodo.org/doi/10.5281/zenodo.10255543)
 
-# How to use PeakPerformance
-For installation instructions, see `Installation.md`.
-For instructions regarding the use of PeakPerformance, check out the example notebook(s) under `notebooks`, the complementary example data under `example`, and the following introductory explanations.
-
-## Preparing raw data
-This step is crucial when using PeakPerformance. Raw data has to be supplied as time series meaning for each signal you want to analyze, save a NumPy array consisting of time in the first dimension and intensity in the second dimension (compare example data). Both time and intensity should also be NumPy arrays. If you e.g. have time and intensity of a singal as lists, you can use the following code to convert, format, and save them in the correct manner:
-
-```python
-import numpy as np
-from pathlib import Path
-
-time_series = np.array([np.array(time), np.array(intensity)])
-np.save(Path(r"example_path/time_series.npy"), time_series)
-```
-
-The naming convention of raw data files is `<acquisition name>_<precursor ion m/z or experiment number>_<product ion m/z start>_<product ion m/z end>.npy`. There should be no underscores within the named sections such as `acquisition name`. Essentially, the raw data names include the acquisition and mass trace, thus yielding a recognizable and unique name for each isotopomer/fragment/metabolite/sample.
-
-## Model selection
-When it comes to selecting models, PeakPerformance has a function performing an automated selection process by analyzing one acquisiton per mass trace with all implemented models. Subsequently, all models are ranked based on an information criterion (either pareto-smoothed importance sampling leave-one-out cross-validation or widely applicable information criterion). For this process to work as intended, you need to specify acquisitions with representative peaks for each mass trace (see example notebook 1). If e.g. most peaks of an analyte show a skewed shape, then select an acquisition where this is the case. For double peaks, select an acquision where the peaks are as distinct and comparable in height as possible.
-Since model selection is a computationally demanding and time consuming process, it is suggested to state the model type as the user (see example notebook 1) if possible.
-
-## Troubleshooting
-### A batch run broke and I want to restart it.
-If an error occured in the middle of a batch run, then you can use the `pipeline_restart` function in the `pipeline` module to create a new batch which will analyze only those samples, which have not been analyzed previously.
-
-### The model parameters don't converge and/or the fit does not describe the raw data well.
-Check the separate file `How to adapt PeakPerformance to your data`.
+# About PeakPerformance
+PeakPerformance employs Bayesian modeling for chromatographic peak data fitting.
+This has the innate advantage of providing uncertainty quantification while jointly estimating all peak parameters united in a single peak model.
+As Markov Chain Monte Carlo (MCMC) methods are utilized to infer the posterior probability distribution, convergence checks and the aformentioned uncertainty quantification are applied as novel quality metrics for a robust peak recognition.
+
+# First steps
+Be sure to check out our thorough [documentation](https://peak-performance.readthedocs.io/en/latest). It contains not only information on how to install PeakPerformance and prepare raw data for its application but also detailed treatises about the implemented model structures, validation with both synthetic and experimental data against a commercially available vendor software, exemplary usage of diagnostic plots and investigation of various effects.
+Furthermore, you will find example notebooks and data sets showcasing different aspects of PeakPerformance.
 
 # How to contribute
 If you encounter bugs while using PeakPerformance, please bring them to our attention by opening an issue. When doing so, describe the problem in detail and add screenshots/code snippets and whatever other helpful material you can provide.

docs/source/index.rst

@@ -35,6 +35,7 @@ The documentation features various notebooks that demonstrate the usage and inve
    :maxdepth: 1
 
    markdown/Installation
+   markdown/Preparing_raw_data
    markdown/Peak_model_composition
    markdown/PeakPerformance_validation
    markdown/PeakPerformance_workflow

docs/source/markdown/Preparing_raw_data.md

@@ -0,0 +1,15 @@
+# Preparing raw data
+
+This step is crucial when using PeakPerformance.
+Raw data has to be supplied as time series meaning for each signal you want to analyze, save a shape `(2, ?)` NumPy array consisting of time in the first, and intensity in the second entry in the first dimension (compare example data in the repository).
+Both time and intensity should also be NumPy arrays.
+If you e.g. have time and intensity of a signal as lists, you can use the following code to convert, format, and save them in the correct manner:
+
+```python
+import numpy as np
+
+time_series = np.array([np.array(time), np.array(intensity)])
+np.save("time_series.npy", time_series)
+```
+
+The naming convention of raw data files is `<acquisition name>_<precursor ion m/z or experiment number>_<product ion m/z start>_<product ion m/z end>.npy`. There should be no underscores within the named sections such as `acquisition name`. Essentially, the raw data names include the acquisition and mass trace, thus yielding a recognizable and unique name for each isotopomer/fragment/metabolite/sample. This is of course only relevant when using the pre-manufactured data pipeline and does not apply to user-generated custom data pipelines.