Repository: MannLabs/alphadia (language: python)

Issue:
DOCS update readme



Discussion / hints:

Fix patch:
diff --git a/.github/workflows/publish_on_pypi.yml b/.github/workflows/publish_on_pypi.yml
index 12405826..1c10e08a 100644
--- a/.github/workflows/publish_on_pypi.yml
+++ b/.github/workflows/publish_on_pypi.yml
@@ -31,7 +31,7 @@ jobs:
         id: get_current_version
         shell: bash -l {0}
         run: |
-         CURRENT_VERSION=$(misc/get_current_version.sh)
+         CURRENT_VERSION=$(./misc/get_current_version.sh)
          echo "new_version=$CURRENT_VERSION" >> $GITHUB_OUTPUT
       - uses: actions/cache@v4
         with:
@@ -50,6 +50,7 @@ jobs:
           twine check dist/*
           conda deactivate
           conda env remove --name alphadia_build -y
+          conda clean --all -y
       - name: Publish distribution to Test-PyPI
         uses: pypa/gh-action-pypi-publish@release/v1
         with:
@@ -65,6 +66,7 @@ jobs:
           alphadia -v
           conda deactivate
           conda env remove --name pip_stable_test -y
+          conda clean --all -y
       - name: Test Test-PyPI stable installation
         shell: bash -le {0}
         run: |
@@ -74,6 +76,7 @@ jobs:
           alphadia -v
           conda deactivate
           conda env remove --name pip_stable_test -y
+          conda clean --all -y
       - name: Publish distribution to PyPI
         uses: pypa/gh-action-pypi-publish@release/v1
         with:
@@ -109,12 +112,14 @@ jobs:
           alphadia -v
           conda deactivate
           conda env remove --name pip_stable -y
+          conda clean --all -y
       - name: Test PyPI loose installation
         shell: bash -le {0}
         run: |
           conda create -n pip_loose python=${{ env.PYTHON_VERSION }} -y
           conda activate pip_loose
-          pip install alphadia==${{ needs.Create_PyPi_Release.outputs.new_version }}"
+          pip install alphadia==${{ needs.Create_PyPi_Release.outputs.new_version }}
           alphadia -v
           conda deactivate
           conda env remove --name pip_loose -y
+          conda clean --all -y
diff --git a/README.md b/README.md
index 4682ca64..2ae200cd 100644
--- a/README.md
+++ b/README.md
@@ -1,9 +1,11 @@
 ![Release](https://img.shields.io/badge/release-v1.6.0-brightgreen)
 ![License](https://img.shields.io/badge/License-Apache-brightgreen)
 ![Tests](https://github.com/MannLabs/alphadia/workflows/Default%20installation%20and%20tests/badge.svg)
+![Docs](https://readthedocs.org/projects/alphadia/badge/?version=latest)
 ![Deployment](https://github.com/MannLabs/alphadia/workflows/Publish%20on%20PyPi%20and%20release%20on%20GitHub/badge.svg)
 ![Coverage](https://github.com/MannLabs/alphadia/blob/main/coverage.svg)
 
+
 <!-- PROJECT LOGO -->
 <br />
 <div align="center">
@@ -14,33 +16,46 @@
   <h3 align="center">alphaDIA</h3>
 
   <p align="center">
-    <a href="https://github.com/MannLabs/alphadia">Preprint</a>
+    <a href="https://www.biorxiv.org/content/10.1101/2024.05.28.596182v1">Preprint</a>
     ·
     <a href="https://github.com/Mannlabs/alphadia/releases/latest">Download</a>
     ·
     <a href="https://github.com/MannLabs/alphadia/discussions">Discussion</a>
     ·
-    <a href="https://github.com/MannLabs/alphadia">Documentation</a>
+    <a href="https://alphadia.readthedocs.io/en/latest/">Documentation</a>
   </p>
 </div>
 
-
 ![preview](assets/preview.gif)
 
-## Table of Contents</summary>
-<ol>
-  <li>
-    <a href="#installation">Installation</a>
-    <ul>
-      <li><a href="#one-click-gui">One-click GUI</a></li>
-      <li><a href="#developer">Developer install</a></li>
-    </ul>
-  </li>
-  <li>
-    <a href="#getting-started">Getting started</a>
-  </li>
-</ol>
-
+## Features
+- Empirical library and fully predicted library search
+- Label free quantification
+- DIA multiplexing
+
+We support the following vendor and processing modes:
+
+| Platform              | Empirical lib | Predicted lib |
+| :---------------- | :------: | :----: |
+| Thermo .raw |   ✅   | ✅ |
+| Sciex .wiff |   ✅   | ✅ |
+| Bruker .d |  ✅   | ⚠️ |
+> ⚠️ **Predicted libraries with Bruker .d data**<br>Alltough search is possible, alphaDIA's feature-free search takes a long time with fully predicted libraries. We are still evaluating how to better support fully predicted libraries.
+
+## Manuscript
+**Please cite:**
+> **AlphaDIA enables End-to-End Transfer Learning for Feature-Free Proteomics**<br>
+> Georg Wallmann, Patricia Skowronek, Vincenth Brennsteiner, Mikhail Lebedev, Marvin Thielert, Sophia Steigerwald, Mohamed Kotb, Tim Heymann, Xie-Xuan Zhou, Magnus Schwörer, Maximilian T. Strauss, Constantin Ammar, Sander Willems, Wen-Feng Zeng, Matthias Mann<br>
+> [biorxiv.org](https://www.biorxiv.org/content/10.1101/2024.05.28.596182v1)
+
+alphaDIA is part of the alphaX ecosystem:
+- [alphaPept](https://github.com/MannLabs/alphapept)
+- [alphaTims](https://github.com/MannLabs/alphatims)
+- [alphaPeptDeep](https://github.com/MannLabs/alphapeptdeep)
+- [alphaRaw](https://github.com/MannLabs/alpharaw)
+- [alphaBase](https://github.com/MannLabs/alphabase)
+- [directLFQ](https://github.com/MannLabs/directlfq)
+- [alphaTims](https://github.com/MannLabs/alphatims)
 
 ## Installation
 
@@ -48,43 +63,36 @@ AlphaDIA can be installed on Windows, macOS and Linux. Please choose the preferr
 
 * [**One-click GUI installation:**](#one-click-gui-installation) Choose this installation if you only want the GUI and/or keep things as simple as possible.
 
-* [**Library and CLI installation:**](#library-and-cli-installation) Choose this installation if you want to use alphaDIA as a library or its command-line interface. You need to be familiar with CLI tools, Conda and Python.
-It allows access to all available features of alphaDIA.
+* [**Pip installation**](#pip-installation)This version allows you to use alphaDIA in your python environment (provided e.g. by conda). You will only have access to the search engine backend and the command line but not the GUI.
 
 * [**Developer installation:**](docs/installation.md#developer-installation) This installation allows to modify alphaDIA's source code directly. Generally, the developer version of alphaDIA outperforms the precompiled versions which makes this the installation of choice for high-throughput experiments.
 
 * [**Docker installation:**](docs/installation.md#use-the-dockerized-version) Choose this for running alphaDIA in a Docker container, which is useful if you want to run it in a cloud environment.
 
+* [**Slurm installation:**](docs/installation.md#slurm-cluster-installation) Choose this for running alphaDIA on a research cluster with Slurm.
 
 ### One-click GUI installation
 Currently available for **MacOS** and **Windows**.
 You can download the latest release of alphaDIA [here](https://github.com/Mannlabs/alphadia/releases/latest).
 
 * **Windows:** Download the latest `win-x64` build. Save it and double click it to install. If you receive a warning during installation click *Run anyway*.
-* **MacOS:** Download the latest `darwin-arm64` build. Please note that alphaDIA currently requires an ARM based M1/2/3 processor for the one-click installer and [mono](#setting-up-mono-macoslinux-only) to be installed. Save the installer and open the parent folder in Finder. Right-click and select *open*. If you receive a warning during installation click *Open*.
+* **MacOS:** Download the latest `darwin-arm64` build. Please note that alphaDIA currently requires an ARM based M1/2/3 processor for the one-click installer. Save the installer and open the parent folder in Finder. Right-click and select *open*. If you receive a warning during installation click *Open*. If you want to use `.raw` files on Thermo instruments alphaRaw is required, which depends on Mono. A detailed guide to installing alphaRaw with mono can be found [here](https://github.com/MannLabs/alpharaw#installation).
 
 As of now, **Linux** users need follow the steps for the
 [developer installation](docs/installation.md#developer-installation) in order to use the GUI.
 
-### Library and CLI installation
+### Pip installation
 If you want to use alphaDIA as a python library (e.g. for importing it into Jupyter notebooks) or only use the command-line interface,
-you can install alphaDIA via `pip`. This is currently also the preferred way for Linux users that do not need the GUI.
+you can install alphaDIA via `pip`.
 
 #### 1. Prerequisites
 Please make sure you have a valid installation of conda or miniconda.
 We recommend setting up miniconda as described on their [website](https://docs.conda.io/projects/miniconda/en/latest/).
 
-##### Setting up mono [MacOS/Linux only]
 If you want to use `.raw` files on Thermo instruments alphaRaw is required, which depends on Mono. A detailed guide to installing alphaRaw with mono can be found [here](https://github.com/MannLabs/alpharaw#installation).
 
 #### 2. Setting up the environment
-
-For any Python package, it is highly recommended to use a separate [conda virtual environment](https://docs.conda.io/en/latest/), as otherwise dependancy conflicts can occur with already existing packages.
-
-```bash
-conda create --name alphadia python=3.9 -y
-conda activate alphadia
-```
+For any Python package, it is highly recommended to use a separate, as otherwise dependancy conflicts can occur with already existing packages. We generally recommend [miniconda](https://docs.anaconda.com/free/miniconda/).
 
 Finally, alphaDIA and all its dependencies can be installed by
 ```bash
diff --git a/alphadia/__init__.py b/alphadia/__init__.py
index 016d81a4..363a78dc 100644
--- a/alphadia/__init__.py
+++ b/alphadia/__init__.py
@@ -1,3 +1,3 @@
 #!python
 
-__version__ = "1.6.1"
+__version__ = "1.6.2"
diff --git a/alphadia/constants/default.yaml b/alphadia/constants/default.yaml
index 6451df9d..c82713c8 100644
--- a/alphadia/constants/default.yaml
+++ b/alphadia/constants/default.yaml
@@ -61,18 +61,39 @@ search_advanced:
   top_k_fragments: 12
 
 calibration:
+
+  # minimum number of times (epochs) the updated calibration target has to been passed
   min_epochs: 3
-  max_epochs: 20
+
+  # Number of precursors searched and scored per batch
   batch_size: 8000
+
+  # recalibration target for the first epoch. For subsequent epochs, the target will increase by this amount.
   recalibration_target: 200
+
+  # TODO: remove as not relevant anymore
+  max_epochs: 20
+
+  # TODO: remove this parameter
   final_full_calibration: False
+
+  # TODO: remove this parameter
   norm_rt_mode: 'linear'
 
 search_initial:
+  # Number of peak groups identified in the convolution score to classify with target decoy comeptition
   initial_num_candidates: 1
+
+  # initial ms1 tolerance in ppm
   initial_ms1_tolerance: 30
+
+  # initial ms2 tolerance in ppm
   initial_ms2_tolerance: 30
+
+  # initial ion mobility tolerance in 1/K_0
   initial_mobility_tolerance: 0.08
+
+  # initial retention time tolerance in seconds
   initial_rt_tolerance: 240
 
 selection_config:
diff --git a/alphadia/libtransform.py b/alphadia/libtransform.py
index b1721720..46125a60 100644
--- a/alphadia/libtransform.py
+++ b/alphadia/libtransform.py
@@ -301,7 +301,11 @@ def forward(self, input: SpecLibBase) -> SpecLibBase:
         # Check if CPU or GPU/MPS should be used
         device = "cpu"
         if self.use_gpu:
-            device = "mps" if os.uname().sysname == "Darwin" else "gpu"
+            try:
+                device = "mps" if os.uname().sysname == "Darwin" else "gpu"
+            except AttributeError:
+                # Windows does not support uname
+                device = "gpu"
 
         model_mgr = ModelManager(device=device)
         if self.checkpoint_folder_path is not None:
diff --git a/docs/_static/css/custom.css b/docs/_static/css/custom.css
index 5cfbfd2e..2289381a 100644
--- a/docs/_static/css/custom.css
+++ b/docs/_static/css/custom.css
@@ -10,3 +10,7 @@
 .autosummary.longtable{
     width: 100%;
 }
+
+.sidebar-brand{
+    width: 50%;
+}
diff --git a/docs/_static/images/methods_calibration.png b/docs/_static/images/methods_calibration.png
new file mode 100644
index 00000000..358380dd
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diff --git a/docs/_static/images/methods_loess.png b/docs/_static/images/methods_loess.png
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diff --git a/docs/_static/images/select_engine.gif b/docs/_static/images/select_engine.gif
new file mode 100644
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diff --git a/docs/_static/logo/alphadia.png b/docs/_static/logo/alphadia.png
new file mode 100644
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diff --git a/docs/_static/logo/logo_black_outline.svg b/docs/_static/logo/logo_black_outline.svg
deleted file mode 100644
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diff --git a/docs/alphadia/hybridselection.rst b/docs/alphadia/hybridselection.rst
deleted file mode 100644
index 6713547c..00000000
--- a/docs/alphadia/hybridselection.rst
+++ /dev/null
@@ -1,9 +0,0 @@
-hybridselection
-===============
-
-Candidate, also called peak group, selection implemented as a weak MS1/MS2 heuristic.
-
-.. automodule:: alphadia.hybridselection
-   :members:
-   :undoc-members:
-   :show-inheritance:
diff --git a/docs/conf.py b/docs/conf.py
index 5591488e..0a31d8c2 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -46,8 +46,8 @@
 ]
 
 html_theme_options = {
-    "light_logo": "logo/logo_black_outline.svg",
-    "dark_logo": "logo/logo_white_outline.svg",
+    "light_logo": "logo/alphadia.png",
+    "dark_logo": "logo/alphadia.png",
     "sidebar_hide_name": True,
 }
 
diff --git a/docs/development.md b/docs/contributing.md
similarity index 98%
rename from docs/development.md
rename to docs/contributing.md
index 51cdbce8..5785698c 100644
--- a/docs/development.md
+++ b/docs/contributing.md
@@ -1,4 +1,4 @@
-# Development
+# Contributing
 
 ## Release a new version
 Note: Releases need to be done from the `main` branch.
diff --git a/docs/index.md b/docs/index.md
index eecd8367..327dae11 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -1,13 +1,13 @@
 
 # AlphaDIA Documentation
-**Version:** 1.6.1 | [Github](https://github.com/MannLabs/alphadia)
+**Version:** 1.6.2 | [Github](https://github.com/MannLabs/alphadia)
 
 Open-source DIA search engine built with the alphaX ecosystem. Built with [alpharaw](https://github.com/MannLabs/alpharaw) and [alphatims](https://github.com/MannLabs/alphatims) for raw file acces. Spectral libraries are predicted with [peptdeep](https://github.com/MannLabs/alphapeptdeep) and managed by [alphabase](https://github.com/MannLabs/alphabase). Quantification is powered by [directLFQ](https://github.com/MannLabs/directLFQ).
 
 **Please cite:**
-> The curse of dimensionality in proteomics – a new processing paradigm for DIA data.<br>
-> Wallmann, Skowronek, XXX Mann<br>
-> bioarxiv.com/XXX
+> **AlphaDIA enables End-to-End Transfer Learning for Feature-Free Proteomics**<br>
+> Georg Wallmann, Patricia Skowronek, Vincenth Brennsteiner, Mikhail Lebedev, Marvin Thielert, Sophia Steigerwald, Mohamed Kotb, Tim Heymann, Xie-Xuan Zhou, Magnus Schwörer, Maximilian T. Strauss, Constantin Ammar, Sander Willems, Wen-Feng Zeng, Matthias Mann<br>
+> [biorxiv.org](https://www.biorxiv.org/content/10.1101/2024.05.28.596182v1)
 
 
 :::{card} Installation
@@ -28,22 +28,13 @@ Introduction to your first DIA search with alphaDIA.
 🔧 Installation<installation>
 🚀 Quickstart<quickstart>
 📚 Guides<guides>
+📖 Methods<methods>
 ```
 
 ```{toctree}
-:caption:
+:caption: Development
 :hidden:
 
-contributing/index
-kitchen-sink/index
-stability
-changelog
-license
-```
-
-```{toctree}
-:caption: 📖 Development
-:hidden:
-contributing
 modules
+contributing
 ```
diff --git a/docs/installation.md b/docs/installation.md
index 603aa787..c145e8a7 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -1,11 +1,59 @@
 # Installation
 
-## Developer installation
+AlphaDIA can be installed on Windows, macOS and Linux. Please choose the preferred installation:
+
+* [**One-click GUI installation:**](#one-click-gui-installation) Choose this installation if you only want the GUI and/or keep things as simple as possible.
+
+* [**Pip installation**](#pip-installation)This version allows you to use alphaDIA as a package within your conda environment. You will only have access to the search engine backend and the command line but not the GUI.
+
+* [**Developer installation:**](#developer-installation) This installation allows to modify alphaDIA's source code directly. Generally, the developer version of alphaDIA outperforms the precompiled versions which makes this the installation of choice for high-throughput experiments.
+
+* [**Docker installation:**](#docker-installation) Choose this for running alphaDIA in a Docker container, which is useful if you want to run it in a cloud environment.
+
+* [**Slurm installation:**](#slurm-cluster-installation) Choose this for running alphaDIA on a research cluster with Slurm.
+
+## One-click GUI installation
+Currently available for **MacOS** and **Windows**.
+You can download the latest release of alphaDIA [here](https://github.com/Mannlabs/alphadia/releases/latest).
+
+* **Windows:** Download the latest `win-x64` build. Save it and double click it to install. If you receive a warning during installation click *Run anyway*.
+* **MacOS:** Download the latest `darwin-arm64` build. Please note that alphaDIA currently requires an ARM based M1/2/3 processor for the one-click installer. Save the installer and open the parent folder in Finder. Right-click and select *open*. If you receive a warning during installation click *Open*. If you want to use `.raw` files on Thermo instruments alphaRaw is required, which depends on Mono. A detailed guide to installing alphaRaw with mono can be found [here](https://github.com/MannLabs/alpharaw#installation).
+
+As of now, **Linux** users need follow the steps for the
+[developer installation](#developer-installation) in order to use the GUI.
+
+## Pip installation
+If you want to use alphaDIA as a python library (e.g. for importing it into Jupyter notebooks) or only use the command-line interface,
+you can install alphaDIA via `pip`.
+
+### 1. Prerequisites
+Please make sure you have a valid installation of conda or miniconda.
+We recommend setting up miniconda as described on their [website](https://docs.conda.io/projects/miniconda/en/latest/).
+
+If you want to use `.raw` files on Thermo instruments alphaRaw is required, which depends on Mono. A detailed guide to installing alphaRaw with mono can be found [here](https://github.com/MannLabs/alpharaw#installation).
+
+### 2. Setting up the environment
+
+Finally, alphaDIA and all its dependencies can be installed by
+```bash
+pip install "alphadia[stable]"
+```
+We strongly recommend using the `stable` version, which has all dependencies fixed,
+for reasons of reproducibility and integrity.
+
+Alternatively, use
+`pip install alphadia`, which comes with less version constraints. This is not recommended, but may be useful to avoid
+version clashes if alphaDIA is imported as a library into a defined python requirement.
+Note however, that this "loose" version might be affected e.g. by breaking changes of third-party dependencies.
+
+Finally, run `alphadia -v` to check if the installation was successful;
+`alphadia -h` will give you a list of command-line options.
 
+
+## Developer installation
 AlphaDIA can be installed in editable (i.e. developer) mode. This allows to fully customize the software and even modify the source code to your specific needs. When an editable Python package is installed, its source code is stored in a location of your choice.
 
-Make sure to first read the prerequisites section [here](../README.md#1-prerequisites)  to have all necessary software
-(conda, mono) installed.
+Make sure you have an conda environment and Mono installed for reading `.raw` files as described [here](https://github.com/MannLabs/alpharaw#installation).
 
 ### 1. Setting up the repository
 
@@ -29,15 +77,14 @@ Use pip to install alphaDIA, using the `development` tag to install additional p
 ```bash
 pip install -e ".[stable,development]"
 ```
-If you need less strict versions for third-party dependencies, use
-`pip install -e ".[development]"`, but make sure to read the corresponding caveats [here](../README.md#3-installation).
+If you need less strict versions for third-party dependencies, use `pip install -e ".[development]"`.
 
-Note: by using the editable flag `-e`, all modifications to the [alphaDIA source code folder](alphadia ) are directly reflected when running alphaDIA. Note that the alphaDIA folder cannot be moved and/or renamed if an editable version is installed.
+Note: by using the editable flag `-e`, all modifications to the alphaDIA source code folder are directly reflected when running alphaDIA. Note that the alphaDIA folder cannot be moved and/or renamed if an editable version is installed.
 
 
 ### 3. Installation: GUI (optional)
 
-If you want to use or extend the GUI, please install NodeJS as described on their  [website](https://nodejs.org/en/download).
+If you want to use or extend the GUI, please install NodeJS as described on their [website](https://nodejs.org/en/download).
 
 Install all frontend packages using npm
 ```bash
@@ -50,7 +97,7 @@ The GUI can then be started by typing
 npm run dev
 ```
 
-## Use the dockerized version
+## Docker Installation
 The containerized version can be used to run alphaDIA e.g. on cloud platforms.
 It can be used to run alphaDIA for multiple input files, as well as for single files only
 (trivial parallelization on computing clusters).
@@ -59,8 +106,11 @@ Note that this container is not optimized neither for performance nor size yet,
 (M1/M2/M3) due to problems with mono. Also, it currently relies on the input files being organized
 in a specific folder structure.
 
-1) Install the latest version of docker (https://docs.docker.com/engine/install/).
-2) Set up your data to match the expected folder structure:
+### 1. Setting up Docker
+Install the latest version of docker (https://docs.docker.com/engine/install/).
+
+### 2. Prepare folder structure
+Set up your data to match the expected folder structure:
 - Create a folder and store its name in a variable, e.g. `DATA_FOLDER=/home/username/data; mkdir -p $DATA_FOLDER`
 - In this folder, create 4 subfolders:
   - `library`: put your library file here, make it writeable for any user (`chmod o+rw *`)
@@ -79,14 +129,14 @@ output_directory: /app/data/output
   The rest of the config values are taken from `default.yaml`, unless you overwrite any value from there
   in your `config.yaml`.
 
-3) Start the container
+### 3. Start the container
 ```bash
 docker run -v $DATA_FOLDER:/app/data/ mannlabs/alphadia:latest
 ```
 After initial download of the container, alphaDIA will start running immediately. Alternatively, you can run an interactive session with
 `docker run -v $DATA_FOLDER:/app/data/ -it mannlabs/alphadia:latest bash`
 
-### Build the image yourself (advanced users)
+### 4. (Advanced) Build the image yourself
 If you want to build the image yourself, you can do so by
 ```bash
 docker build -t alphadia-docker .
@@ -96,20 +146,19 @@ and run it with
 docker run -v $DATA_FOLDER:/app/data/ --rm alphadia-docker
 ```
 
-## Installing alphaDIA on a SLURM cluster
+## Slurm cluster Installation
 
-### 1. Prerequisites
-Check the prerequisites section [here](../README.md#1-prerequisites).
-
-### 2. Set up environment
-Create and activate a conda environment as described [here](../README.md#2-setting-up-the-environment).
-
-### 3. Installing mono
+### 1. Set up environment
+Create and activate a new conda environment
+```bash
+conda create -n alpha
+```
+### 2. Installing mono
 Install mono to support reading proprietary vendor formats like Thermo `.raw` files.
 
 Please make sure you include the conda-forge channel
 ```bash
-conda install python=3.9 -c conda-forge
+conda install python=3.11 -c conda-forge
 ```
 Then install mono by
 ```bash
@@ -138,9 +187,11 @@ Copyright (C) 2002-2014 Novell, Inc, Xamarin Inc and Contributors. www.mono-proj
 ```
 
 
-### 4. Installing alphaDIA
-
-Install alphaDIA using pip as described [here](../README.md#3-installation).
+### 3. Installing alphaDIA
 
+Install alphaDIA using pip:
+```bash
+pip install "alphadia[stable]"
+```
 Afterwards, verify the alphaDIA installation by running:
-`alphadia -h` which should give an output like this ```1.5.5```.
+`alphadia --version` which should output the current version.
diff --git a/docs/methods.md b/docs/methods.md
new file mode 100644
index 00000000..762dd2e8
--- /dev/null
+++ b/docs/methods.md
@@ -0,0 +1,9 @@
+# Technlogy
+
+```{toctree}
+    :maxdepth: 1
+methods/command-line
+methods/configuration
+methods/calibration
+
+```
diff --git a/docs/methods/calibration.md b/docs/methods/calibration.md
new file mode 100644
index 00000000..3363735f
--- /dev/null
+++ b/docs/methods/calibration.md
@@ -0,0 +1,102 @@
+# Calibration
+
+## Calibration and optimization
+The first step of every alphaDIA run entails the calibration of the empirical or fully predicted spectral library to the observed values. This step has two main purposes: 1. Removing the systematic deviation of observed and library values and 2. Optimizing the size of the search space to reflect the expected deviation from the library values. For DIA search this means calibration and optimization of retention time, ion mobility, precursor m/z and fragment m/z.
+
+AlphaDIA uses a iterative calibration schedule consisting of epochs. Every epoch, a defined number of precursors need to be identified at 1% FDR before calibration and optimization is performed and the next epoch is started (dashed green line). The number of precursors for the `n`'th epoch is derived from the parameter `recalibration_target` (default: 200) as `n*recalibration_target`. To reach this target library entries are searched, scored and aggregated, following an exponential batch plan (blue).
+
+<img src="../_static/images/methods_calibration.png" width="100%" height="auto">
+
+If one epoch has accumulated enough confident target precursors, they are calibrated to the observed values using locally estimated scatterplot smoothing (LOESS) regression. For calibration of fragment m/z values, a fixed number of up to 5000 (but at least 500) of the best fragments according to their  XIC correlation are used. For precursor calibration all precursors passing 1% FDR are used.
+For optimizing the search space, tolerances like retention time, ion mobility and m/z ratios need to be reduced. The goal is to cover the expected spectrum space but reduce it as much as possible to accelerate search and gain statsitical power. Starting with initial tolerances defined in `search_initial`.  At the end of every epoch the 95% deviation after calibration is adopted as new tolerance until the target tolerances defined in the `search` section are reached.
+
+The search is finished as soon as the minimum numberof epochs `min_epochs` has passed and all tolerances have been to the target tolerances defined in `search`.
+## Configuring the calibration schedule
+
+```yaml
+calibration:
+  # minimum number of times (epochs) the updated calibration target has to been passed
+  min_epochs: 3
+
+  # Number of precursors searched and scored per batch
+  batch_size: 8000
+
+  # recalibration target for the first epoch. For subsequent epochs, the target will increase by this amount.
+  recalibration_target: 200
+
+search_initial:
+  # Number of peak groups identified in the convolution score to classify with target decoy comeptition
+  initial_num_candidates: 1
+
+  # initial ms1 tolerance in ppm
+  initial_ms1_tolerance: 30
+
+  # initial ms2 tolerance in ppm
+  initial_ms2_tolerance: 30
+
+  # initial ion mobility tolerance in 1/K_0
+  initial_mobility_tolerance: 0.08
+
+  # initial retention time tolerance in seconds
+  initial_rt_tolerance: 240
+```
+
+## Calibration using LOESS
+Individual properties like the retention time deviate from their library values and need to be calibrated (a). As a nonlinear but stable method, locally estimated scatterplot smoothing (LOESS) using both density and uniformly distributed kernels is used. (b) A collection of polynomial kernels is fitted to uniformly distributed subregions of the data. These consist of first and second degree polynomials basis functions of the calibratable property. (c) The individual functions are combined and smoothed using tricubic weights. (d) Combining the kernels with their weighting functions allows to approximate the systematic deviation of the data locally. (e), The sum of the weighted kernels can then be used for continuous approximation and calibration of retention times. The architecture is built on the scikit-learn package and can be configured to use different hyperparameters and arbitrary predictors for calibration.
+
+<img src="../_static/images/methods_loess.png" width="100%" height="auto">
+
+## Configuring the LOESS model
+
+The type of model, the hyperparameters and the columns used as input and target for calibration can be set in the `calibration_manager` section of the configuration file.
+
+```yaml
+calibration_manager:
+  - name: fragment
+    estimators:
+      - name: mz
+        model: LOESSRegression
+        model_args:
+          n_kernels: 2
+        input_columns:
+          - mz_library
+        target_columns:
+          - mz_observed
+        output_columns:
+          - mz_calibrated
+        transform_deviation: 1e6
+  - name: precursor
+    estimators:
+        - name: mz
+          model: LOESSRegression
+          model_args:
+            n_kernels: 2
+          input_columns:
+            - mz_library
+          target_columns:
+            - mz_observed
+          output_columns:
+            - mz_calibrated
+          transform_deviation: 1e6
+        - name: rt
+          model: LOESSRegression
+          model_args:
+            n_kernels: 6
+          input_columns:
+            - rt_library
+          target_columns:
+            - rt_observed
+          output_columns:
+            - rt_calibrated
+        - name: mobility
+          model: LOESSRegression
+          model_args:
+            n_kernels: 2
+          input_columns:
+            - mobility_library
+          target_columns:
+            - mobility_observed
+          output_columns:
+            - mobility_calibrated
+
+```
diff --git a/docs/methods/command-line.md b/docs/methods/command-line.md
new file mode 100644
index 00000000..20267ed6
--- /dev/null
+++ b/docs/methods/command-line.md
@@ -0,0 +1,124 @@
+# Command line
+AlphaDIA offers a command line interface to perform searches. In fact, even the GUI uses the command line interface internally. For reprodicable searches, method optimization or cluster and cloud use, the command line can be quite usefull.
+
+## Usage
+Before starting, please make sure that alphaDIA is correctly installed and up to date.
+```bash
+alphadia -v
+```
+
+To get an overview of all possible arguments, you can use
+
+```bash
+alphadia -h
+```
+
+Which should return
+
+```
+usage: alphadia [-h] [--version] [--output [OUTPUT]] [--file FILE]
+                [--directory DIRECTORY] [--regex [REGEX]]
+                [--library [LIBRARY]] [--fasta FASTA] [--config [CONFIG]]
+                [--wsl] [--config-dict [CONFIG_DICT]]
+
+Search DIA experiments with alphaDIA
+
+options:
+  -h, --help            show this help message and exit
+  --version, -v         Print version and exit
+  --output [OUTPUT], -o [OUTPUT]
+                        Output directory
+  --file FILE, -f FILE  Raw data input files.
+  --directory DIRECTORY, -d DIRECTORY
+                        Directory containing raw data input files.
+  --regex [REGEX], -r [REGEX]
+                        Regex to match raw files in directory.
+  --library [LIBRARY], -l [LIBRARY]
+                        Spectral library.
+  --fasta FASTA         Fasta file(s) used to generate or annotate the
+                        spectral library.
+  --config [CONFIG], -c [CONFIG]
+                        Config yaml which will be used to update the default
+                        config.
+  --wsl, -w             Set if running on Windows Subsystem for Linux.
+  --config-dict [CONFIG_DICT]
+                        Python Dict which will be used to update the default
+                        config.
+```
+
+## Setting up a search
+The follwoing section will focus on how to set up a search using bash on mac or linux. For windows, please use a `.ps1` powershell script.
+Create a new bash script named `search.sh` and enter it.
+
+### 1. Basics
+```bash
+vim search.sh
+```
+We will write the command across multiple lines to make it more legible.
+First, choose an output folder with the `-o` option, where all of the search results will be stored.
+
+```
+alphadia \
+    -o /my/folder/output \
+```
+
+### 2. Input Files
+Next add some raw files to the search with the `-f` option
+
+```
+    -f /my/raw/files1/exploris007_experiment1_rep1.raw \
+    -f /my/raw/files1/exploris007_experiment1_rep2.raw \
+```
+AlphaDIA also supports to add whole directories to the search using the `d` option
+```
+    -d /my/raw/files2 \
+```
+
+Furthermore, files can be filtered by providing a regex filter using `--regex`. This allows for example to select a mass spec output folder and select by the experiment.
+```
+    --regex ".*_experiment1_.*\.raw$" \
+```
+
+### 3. Libraries
+You can provide alphaDIA with an existing alphaBase `.hdf` or generic `.tsv` library.
+Use the `--library` option to provide a library location
+```
+    --library speclib.single_species.hdf \
+```
+
+You can also provide a fasta file for reannotation of the library using the `--fasta` option.
+Please note, if library prediction is enabled, the fasta file will not only be used for reannotation but will be digested and in-silico prediction of spectral library will be performed with peptDeep.
+
+```
+    --fasta human_reviewed_2024_03_11.fasta \
+```
+
+### 4. Configuration
+To control the behaviour of alphaDIA's search, you will want to provide a `config.yaml` file.
+All settings defined in this file will update the default parameters of alphaDIA.
+
+You can either write it from scratch or use a `config.yaml` generated in a GUI search.
+
+```
+    --config "config_astral_first_pass.yaml" \
+```
+
+Sometimes, for example when optimizing a single parameter or building multi step workflows, you want to change only a single parameters on top of an existing configuration. Therefore, alphaDIA offers the `--config-dict` option, which leds you access all parameters from the command line.
+```
+    --config-dict "{'library_prediction':{'nce':26}}"
+```
+
+### 5. Summary
+The full script looks like:
+```bash
+alphadia \
+    -o /my/folder/output \
+    -f /my/raw/files1/exploris007_experiment1_rep1.raw \
+    -f /my/raw/files1/exploris007_experiment1_rep2.raw \
+    -d /my/raw/files2 \
+    --regex ".*_experiment1_.*\.raw$" \
+    --library speclib.single_species.hdf \
+    --fasta human_reviewed_2024_03_11.fasta \
+    --config config_astral_first_pass.yaml \
+    --config-dict "{'library_prediction':{'nce':26}}"
+```
diff --git a/docs/methods/configuration.md b/docs/methods/configuration.md
new file mode 100644
index 00000000..a025a48b
--- /dev/null
+++ b/docs/methods/configuration.md
@@ -0,0 +1,1 @@
+# Configuration
diff --git a/docs/modules.rst b/docs/modules.rst
index c8cc1caf..c00e033d 100644
--- a/docs/modules.rst
+++ b/docs/modules.rst
@@ -10,7 +10,6 @@ Modules
     alphadia/features
     alphadia/fragcomp
     alphadia/grouping
-    alphadia/hybridselection
     alphadia/libtransform
     alphadia/outputtransform
     alphadia/planning
diff --git a/docs/quickstart.md b/docs/quickstart.md
index acb98436..6502e055 100644
--- a/docs/quickstart.md
+++ b/docs/quickstart.md
@@ -1,1 +1,29 @@
 # Quickstart
+
+The following guide will show how to search example Orbitrap Astral Data with alphaDIA.
+
+## 1. Prerequisites
+Make sure you have installed the GUI using the one-click installer. To verify your installation, open alphaDIA and make sure that the `BundledExecutionEngine` is selected.
+
+<img src="_static/images/select_engine.gif" width="400" height="auto">
+
+## 2. Test data
+
+For the first search we will be using a spectral library to search 60SPD bulk HeLa samples on the Orbitrap Astral. Download the test samples and save them: [HeLa library](https://datashare.biochem.mpg.de/s/Uw2yfNSbApfPpTk), [RAW files].(https://datashare.biochem.mpg.de/s/339jg5HtGrwLwDN)
+
+## 3. Search settings
+
+### Input Files
+Import the library `.hdf` file and select the thre `.raw` files. You can select a human `.fasta` file for reannotation but it's generally not recommended for empirical spectral libraries.
+
+### Method Settings
+Although alphaDIA is highly customizable, we will only specify a limited number of settings here. Go to the *Search* settings and make the following changes:
+* Number of candidates: 5
+* MS1 Tolerance 4ppm
+* MS2 Tolerance 7ppm
+
+### Output files
+Select an output folder where the search progress and the final results should be saved.
+
+## 4. Run the search
+Click *Run Workflow* to start the search and see the progress.
diff --git a/gui/package.json b/gui/package.json
index be359fb3..bd21cbba 100644
--- a/gui/package.json
+++ b/gui/package.json
@@ -1,7 +1,7 @@
 {
   "name": "alphadia",
   "productName": "alphadia-gui",
-  "version": "1.6.1",
+  "version": "1.6.2",
   "description": "Graphical user interface for DIA data analysis",
   "main": "dist/electron.js",
   "homepage": "./",
diff --git a/gui/src/main/modules/profile.js b/gui/src/main/modules/profile.js
index e10db634..e92663f6 100644
--- a/gui/src/main/modules/profile.js
+++ b/gui/src/main/modules/profile.js
@@ -7,7 +7,7 @@ const { dialog } = require('electron')
 const Profile = class {
 
     config = {
-        "version": "1.6.1",
+        "version": "1.6.2",
         "conda": {
             "envName": "alpha",
             "path": ""
diff --git a/misc/.bumpversion.cfg b/misc/.bumpversion.cfg
index 7ba11093..33021c64 100644
--- a/misc/.bumpversion.cfg
+++ b/misc/.bumpversion.cfg
@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 1.6.1
+current_version = 1.6.2
 commit = True
 tag = True
 parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)(\-(?P<release>[a-z]+)(?P<build>\d+))?
diff --git a/nbs/tutorial_nbs/finetuning.ipynb b/nbs/tutorial_nbs/finetuning.ipynb
index 936d2d17..5e0c2aef 100644
--- a/nbs/tutorial_nbs/finetuning.ipynb
+++ b/nbs/tutorial_nbs/finetuning.ipynb
@@ -28,240 +28,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "transfer_lib = SpecLibBase()\n",
-    "transfer_lib.load_hdf('C:/Users/USER/Desktop/Germany/work/MPIB/alphaDia/second_pass_score/second_pass_score/output/speclib.transfer.hdf', load_mod_seq=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
     "transfer_lib = SpecLibBase()\n",
-    "transfer_lib.load_hdf('C:/Users/USER/Desktop/Germany/work/MPIB/alphaDia/d0_search/output/speclib.transfer.hdf', load_mod_seq=True)"
+    "transfer_lib.load_hdf('/Users/georgwallmann/Documents/data/alphadia_manuscript/2024_04_25_Dimethyl_GPF/transfer_learning_asms/speclib.transfer.hdf', load_mod_seq=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>charge</th>\n",
-       "      <th>frag_start_idx</th>\n",
-       "      <th>frag_stop_idx</th>\n",
-       "      <th>mobility</th>\n",
-       "      <th>mod_seq_charge_hash</th>\n",
-       "      <th>mod_seq_hash</th>\n",
-       "      <th>nAA</th>\n",
-       "      <th>precursor_mz</th>\n",
-       "      <th>rt</th>\n",
-       "      <th>genes</th>\n",
-       "      <th>...</th>\n",
-       "      <th>mz_observed</th>\n",
-       "      <th>precursor_idx</th>\n",
-       "      <th>proba</th>\n",
-       "      <th>proteins</th>\n",
-       "      <th>rt_calibrated</th>\n",
-       "      <th>rt_library</th>\n",
-       "      <th>rt_norm</th>\n",
-       "      <th>rt_observed</th>\n",
-       "      <th>sequence</th>\n",
-       "      <th>use_for_ms2</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>0.000001</td>\n",
-       "      <td>16567173003293321201</td>\n",
-       "      <td>16567173003293321199</td>\n",
-       "      <td>7</td>\n",
-       "      <td>450.281660</td>\n",
-       "      <td>788.07110</td>\n",
-       "      <td>SKIL_HUMAN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>450.28150</td>\n",
-       "      <td>863296</td>\n",
-       "      <td>0.048353</td>\n",
-       "      <td>P12757</td>\n",
-       "      <td>973.81915</td>\n",
-       "      <td>1155.54150</td>\n",
-       "      <td>0.345942</td>\n",
-       "      <td>788.07110</td>\n",
-       "      <td>ILEEVLR</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>6</td>\n",
-       "      <td>12</td>\n",
-       "      <td>0.000001</td>\n",
-       "      <td>4618820694754887703</td>\n",
-       "      <td>4618820694754887701</td>\n",
-       "      <td>7</td>\n",
-       "      <td>397.753404</td>\n",
-       "      <td>423.29166</td>\n",
-       "      <td>SHSA4_HUMAN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>397.75214</td>\n",
-       "      <td>216536</td>\n",
-       "      <td>0.125965</td>\n",
-       "      <td>Q96DD7</td>\n",
-       "      <td>424.34580</td>\n",
-       "      <td>740.40420</td>\n",
-       "      <td>0.217576</td>\n",
-       "      <td>423.29166</td>\n",
-       "      <td>PPAGLRR</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>12</td>\n",
-       "      <td>18</td>\n",
-       "      <td>0.000001</td>\n",
-       "      <td>14815979281979287173</td>\n",
-       "      <td>14815979281979287171</td>\n",
-       "      <td>7</td>\n",
-       "      <td>385.213778</td>\n",
-       "      <td>403.07294</td>\n",
-       "      <td>PIRC1_HUMAN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>385.21380</td>\n",
-       "      <td>216104</td>\n",
-       "      <td>0.124993</td>\n",
-       "      <td>Q5BN46</td>\n",
-       "      <td>451.63693</td>\n",
-       "      <td>715.64435</td>\n",
-       "      <td>0.192627</td>\n",
-       "      <td>403.07294</td>\n",
-       "      <td>ATAPPER</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2</td>\n",
-       "      <td>18</td>\n",
-       "      <td>24</td>\n",
-       "      <td>0.000001</td>\n",
-       "      <td>16519725553471297779</td>\n",
-       "      <td>16519725553471297777</td>\n",
-       "      <td>7</td>\n",
-       "      <td>443.273835</td>\n",
-       "      <td>401.38837</td>\n",
-       "      <td>TRM44_HUMAN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>443.27356</td>\n",
-       "      <td>782703</td>\n",
-       "      <td>0.125095</td>\n",
-       "      <td>Q8IYL2</td>\n",
-       "      <td>486.54074</td>\n",
-       "      <td>690.53510</td>\n",
-       "      <td>0.177122</td>\n",
-       "      <td>401.38837</td>\n",
-       "      <td>TKQPEAK</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2</td>\n",
-       "      <td>24</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.000001</td>\n",
-       "      <td>3187609901239812214</td>\n",
-       "      <td>3187609901239812212</td>\n",
-       "      <td>7</td>\n",
-       "      <td>391.213778</td>\n",
-       "      <td>320.52686</td>\n",
-       "      <td>MTX1_HUMAN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>391.21393</td>\n",
-       "      <td>821815</td>\n",
-       "      <td>0.125102</td>\n",
-       "      <td>Q13505</td>\n",
-       "      <td>423.20860</td>\n",
-       "      <td>741.59850</td>\n",
-       "      <td>0.164096</td>\n",
-       "      <td>320.52686</td>\n",
-       "      <td>PPSPEAR</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 27 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   charge  frag_start_idx  frag_stop_idx  mobility   mod_seq_charge_hash  \\\n",
-       "0       2               0              6  0.000001  16567173003293321201   \n",
-       "1       2               6             12  0.000001   4618820694754887703   \n",
-       "2       2              12             18  0.000001  14815979281979287173   \n",
-       "3       2              18             24  0.000001  16519725553471297779   \n",
-       "4       2              24             30  0.000001   3187609901239812214   \n",
-       "\n",
-       "           mod_seq_hash  nAA  precursor_mz         rt        genes  ...  \\\n",
-       "0  16567173003293321199    7    450.281660  788.07110   SKIL_HUMAN  ...   \n",
-       "1   4618820694754887701    7    397.753404  423.29166  SHSA4_HUMAN  ...   \n",
-       "2  14815979281979287171    7    385.213778  403.07294  PIRC1_HUMAN  ...   \n",
-       "3  16519725553471297777    7    443.273835  401.38837  TRM44_HUMAN  ...   \n",
-       "4   3187609901239812212    7    391.213778  320.52686   MTX1_HUMAN  ...   \n",
-       "\n",
-       "   mz_observed  precursor_idx     proba proteins  rt_calibrated  rt_library  \\\n",
-       "0    450.28150         863296  0.048353   P12757      973.81915  1155.54150   \n",
-       "1    397.75214         216536  0.125965   Q96DD7      424.34580   740.40420   \n",
-       "2    385.21380         216104  0.124993   Q5BN46      451.63693   715.64435   \n",
-       "3    443.27356         782703  0.125095   Q8IYL2      486.54074   690.53510   \n",
-       "4    391.21393         821815  0.125102   Q13505      423.20860   741.59850   \n",
-       "\n",
-       "    rt_norm  rt_observed  sequence  use_for_ms2  \n",
-       "0  0.345942    788.07110   ILEEVLR         True  \n",
-       "1  0.217576    423.29166   PPAGLRR        False  \n",
-       "2  0.192627    403.07294   ATAPPER        False  \n",
-       "3  0.177122    401.38837   TKQPEAK        False  \n",
-       "4  0.164096    320.52686   PPSPEAR        False  \n",
-       "\n",
-       "[5 rows x 27 columns]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/georgwallmann/Documents/git/alphabase/alphabase/peptide/precursor.py:27: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  df.fillna(\"\", inplace=True)\n",
+      "/Users/georgwallmann/Documents/git/alphabase/alphabase/peptide/precursor.py:48: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  df.sort_values(\"nAA\", inplace=True)\n"
+     ]
     }
    ],
    "source": [
-    "transfer_lib = SpecLibBase()\n",
-    "transfer_lib.load_hdf('C:/Users/USER/Desktop/Germany/work/MPIB/alphaDia/speclib.transfer.hdf', load_mod_seq=True)\n",
-    "transfer_lib.precursor_df.head()"
+    "transfer_lib.precursor_df = transfer_lib.precursor_df[~transfer_lib.precursor_df['mods'].str.contains('Dimethyl@C')]"
    ]
   },
   {
@@ -273,21 +71,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2024-05-19 00:52:54> Predicting RT ...\n"
+      "2024-05-30 16:50:54> Predicting RT ...\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 29/29 [00:14<00:00,  2.02it/s]\n"
+      "/Users/georgwallmann/Documents/git/alphapeptdeep/peptdeep/model/model_interface.py:990: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  precursor_df[self.target_column_to_predict] = 0.0\n",
+      "100%|██████████| 29/29 [00:22<00:00,  1.30it/s]\n",
+      "/Users/georgwallmann/Documents/git/alphapeptdeep/peptdeep/pretrained_models.py:901: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  df['rt_norm_pred'] = df.rt_pred\n",
+      "/Users/georgwallmann/Documents/git/alphabase/alphabase/peptide/precursor.py:27: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  df.fillna(\"\", inplace=True)\n"
      ]
     },
     {
@@ -296,13 +111,13 @@
        "Text(0, 0.5, 'RT predicted')"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -314,7 +129,7 @@
    "source": [
     "\n",
     "tune_mgr = FinetuneManager(\n",
-    "    device=\"gpu\",\n",
+    "    device=\"mps\",\n",
     "    settings=settings)\n",
     "tune_mgr.nce = 25\n",
     "tune_mgr.instrument = 'Lumos'\n",
@@ -326,105 +141,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2024-05-19 01:07:14>  Epoch -1  Lr: 0.00000   Training loss: 0.0000   Test loss: 0.2368\n",
-      "2024-05-19 01:07:14>  Fine-tuning RT model\n",
-      "2024-05-19 01:07:17>  Epoch 0   Lr: 0.00010   Training loss: 0.1624   Test loss: 0.1288\n",
-      "2024-05-19 01:07:20>  Epoch 1   Lr: 0.00020   Training loss: 0.0817   Test loss: 0.0519\n",
-      "2024-05-19 01:07:22>  Epoch 2   Lr: 0.00030   Training loss: 0.0353   Test loss: 0.0298\n",
-      "2024-05-19 01:07:24>  Epoch 3   Lr: 0.00040   Training loss: 0.0256   Test loss: 0.0247\n",
-      "2024-05-19 01:07:27>  Epoch 4   Lr: 0.00050   Training loss: 0.0220   Test loss: 0.0212\n",
-      "2024-05-19 01:07:29>  Epoch 5   Lr: 0.00050   Training loss: 0.0192   Test loss: 0.0197\n",
-      "2024-05-19 01:07:32>  Epoch 6   Lr: 0.00050   Training loss: 0.0174   Test loss: 0.0184\n",
-      "2024-05-19 01:07:34>  Epoch 7   Lr: 0.00050   Training loss: 0.0158   Test loss: 0.0163\n",
-      "2024-05-19 01:07:36>  Epoch 8   Lr: 0.00050   Training loss: 0.0146   Test loss: 0.0152\n",
-      "2024-05-19 01:07:38>  Epoch 9   Lr: 0.00050   Training loss: 0.0140   Test loss: 0.0159\n",
-      "2024-05-19 01:07:41>  Epoch 10  Lr: 0.00050   Training loss: 0.0138   Test loss: 0.0143\n",
-      "2024-05-19 01:07:43>  Epoch 11  Lr: 0.00050   Training loss: 0.0130   Test loss: 0.0144\n",
-      "2024-05-19 01:07:46>  Epoch 12  Lr: 0.00050   Training loss: 0.0127   Test loss: 0.0166\n",
-      "2024-05-19 01:07:48>  Epoch 13  Lr: 0.00050   Training loss: 0.0131   Test loss: 0.0134\n",
-      "2024-05-19 01:07:50>  Epoch 14  Lr: 0.00050   Training loss: 0.0118   Test loss: 0.0129\n",
-      "2024-05-19 01:07:52>  Epoch 15  Lr: 0.00050   Training loss: 0.0115   Test loss: 0.0148\n",
-      "2024-05-19 01:07:55>  Epoch 16  Lr: 0.00050   Training loss: 0.0122   Test loss: 0.0155\n",
-      "2024-05-19 01:07:57>  Epoch 17  Lr: 0.00050   Training loss: 0.0150   Test loss: 0.0159\n",
-      "2024-05-19 01:08:00>  Epoch 18  Lr: 0.00050   Training loss: 0.0130   Test loss: 0.0130\n",
-      "2024-05-19 01:08:02>  Epoch 19  Lr: 0.00050   Training loss: 0.0108   Test loss: 0.0122\n",
-      "2024-05-19 01:08:04>  Epoch 20  Lr: 0.00050   Training loss: 0.0103   Test loss: 0.0120\n",
-      "2024-05-19 01:08:07>  Epoch 21  Lr: 0.00050   Training loss: 0.0105   Test loss: 0.0149\n",
-      "2024-05-19 01:08:09>  Epoch 22  Lr: 0.00050   Training loss: 0.0119   Test loss: 0.0128\n",
-      "2024-05-19 01:08:11>  Epoch 23  Lr: 0.00050   Training loss: 0.0101   Test loss: 0.0123\n",
-      "2024-05-19 01:08:14>  Epoch 24  Lr: 0.00050   Training loss: 0.0100   Test loss: 0.0118\n",
-      "2024-05-19 01:08:16>  Epoch 25  Lr: 0.00050   Training loss: 0.0095   Test loss: 0.0117\n",
-      "2024-05-19 01:08:18>  Epoch 26  Lr: 0.00050   Training loss: 0.0092   Test loss: 0.0115\n",
-      "2024-05-19 01:08:21>  Epoch 27  Lr: 0.00050   Training loss: 0.0092   Test loss: 0.0111\n",
-      "2024-05-19 01:08:23>  Epoch 28  Lr: 0.00050   Training loss: 0.0094   Test loss: 0.0117\n",
-      "2024-05-19 01:08:26>  Epoch 29  Lr: 0.00050   Training loss: 0.0093   Test loss: 0.0110\n",
-      "2024-05-19 01:08:28>  Epoch 30  Lr: 0.00050   Training loss: 0.0091   Test loss: 0.0120\n",
-      "2024-05-19 01:08:30>  Epoch 31  Lr: 0.00050   Training loss: 0.0089   Test loss: 0.0108\n",
-      "2024-05-19 01:08:33>  Epoch 32  Lr: 0.00050   Training loss: 0.0102   Test loss: 0.0146\n",
-      "2024-05-19 01:08:35>  Epoch 33  Lr: 0.00050   Training loss: 0.0102   Test loss: 0.0107\n",
-      "2024-05-19 01:08:37>  Epoch 34  Lr: 0.00050   Training loss: 0.0093   Test loss: 0.0120\n",
-      "2024-05-19 01:08:40>  Epoch 35  Lr: 0.00025   Training loss: 0.0093   Test loss: 0.0130\n",
-      "2024-05-19 01:08:42>  Epoch 36  Lr: 0.00025   Training loss: 0.0093   Test loss: 0.0104\n",
-      "2024-05-19 01:08:44>  Epoch 37  Lr: 0.00025   Training loss: 0.0080   Test loss: 0.0102\n",
-      "2024-05-19 01:08:47>  Epoch 38  Lr: 0.00025   Training loss: 0.0083   Test loss: 0.0103\n",
-      "2024-05-19 01:08:49>  Epoch 39  Lr: 0.00025   Training loss: 0.0077   Test loss: 0.0101\n",
-      "2024-05-19 01:08:52>  Epoch 40  Lr: 0.00025   Training loss: 0.0077   Test loss: 0.0105\n",
-      "2024-05-19 01:08:54>  Epoch 41  Lr: 0.00025   Training loss: 0.0078   Test loss: 0.0102\n",
-      "2024-05-19 01:08:56>  Epoch 42  Lr: 0.00025   Training loss: 0.0075   Test loss: 0.0100\n",
-      "2024-05-19 01:08:59>  Epoch 43  Lr: 0.00025   Training loss: 0.0077   Test loss: 0.0107\n",
-      "2024-05-19 01:09:01>  Epoch 44  Lr: 0.00025   Training loss: 0.0078   Test loss: 0.0107\n",
-      "2024-05-19 01:09:03>  Epoch 45  Lr: 0.00025   Training loss: 0.0077   Test loss: 0.0102\n",
-      "2024-05-19 01:09:06>  Epoch 46  Lr: 0.00025   Training loss: 0.0074   Test loss: 0.0099\n",
-      "2024-05-19 01:09:08>  Epoch 47  Lr: 0.00025   Training loss: 0.0075   Test loss: 0.0105\n",
-      "2024-05-19 01:09:11>  Epoch 48  Lr: 0.00025   Training loss: 0.0078   Test loss: 0.0100\n",
-      "2024-05-19 01:09:13>  Epoch 49  Lr: 0.00025   Training loss: 0.0076   Test loss: 0.0098\n",
-      "2024-05-19 01:09:15>  Epoch 50  Lr: 0.00025   Training loss: 0.0073   Test loss: 0.0099\n",
-      "2024-05-19 01:09:15>  Fine tuning finished at  epoch: 51 \n",
-      " train_loss: nan \n",
-      " learning_rate: nan \n",
-      " test_loss: 0.00987 \n",
-      " test_r_square: 0.99004 \n",
-      " test_r: 0.99501 \n",
-      " test_slope: 0.99347 \n",
-      " test_intercept: 0.00401 \n",
-      " abs_error_95th_percentile: 0.02663 \n",
-      "\n",
-      "2024-05-19 01:09:15> Predicting RT ...\n"
+      "2024-05-30 16:51:30>  Epoch -1  Lr: 0.00000   Training loss: 0.0000   Test loss: 0.2415\n",
+      "2024-05-30 16:51:30>  Fine-tuning RT model\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 29/29 [00:01<00:00, 15.31it/s]\n"
+      "/Users/georgwallmann/Documents/git/alphadia/alphadia/transferlearning/train.py:147: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  or abs(val_loss - self.last_loss) / self.last_loss < self.margin\n",
+      "/Users/georgwallmann/miniconda3/envs/alpha/lib/python3.11/site-packages/torch/optim/lr_scheduler.py:28: UserWarning: The verbose parameter is deprecated. Please use get_last_lr() to access the learning rate.\n",
+      "  warnings.warn(\"The verbose parameter is deprecated. Please use get_last_lr() \"\n",
+      "/Users/georgwallmann/miniconda3/envs/alpha/lib/python3.11/site-packages/torch/optim/lr_scheduler.py:156: UserWarning: The epoch parameter in `scheduler.step()` was not necessary and is being deprecated where possible. Please use `scheduler.step()` to step the scheduler. During the deprecation, if epoch is different from None, the closed form is used instead of the new chainable form, where available. Please open an issue if you are unable to replicate your use case: https://github.com/pytorch/pytorch/issues/new/choose.\n",
+      "  warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)\n"
      ]
     },
     {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'RT predicted')"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2024-05-30 16:52:11>  Epoch 0   Lr: 0.00010   Training loss: 0.1128   Test loss: 0.0450\n"
+     ]
     },
     {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/georgwallmann/miniconda3/envs/alpha/lib/python3.11/site-packages/torch/optim/lr_scheduler.py:156: UserWarning: The epoch parameter in `scheduler.step()` was not necessary and is being deprecated where possible. Please use `scheduler.step()` to step the scheduler. During the deprecation, if epoch is different from None, the closed form is used instead of the new chainable form, where available. Please open an issue if you are unable to replicate your use case: https://github.com/pytorch/pytorch/issues/new/choose.\n",
+      "  warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2024-05-30 16:52:54>  Epoch 1   Lr: 0.00020   Training loss: 0.0386   Test loss: 0.0342\n"
+     ]
     }
    ],
    "source": [
@@ -988,7 +748,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.18"
+   "version": "3.11.7"
   }
  },
  "nbformat": 4,
diff --git a/release/macos/build_backend_macos.sh b/release/macos/build_backend_macos.sh
index 1fa77004..6a8b4d14 100755
--- a/release/macos/build_backend_macos.sh
+++ b/release/macos/build_backend_macos.sh
@@ -6,7 +6,7 @@ python -c "from huggingface_hub import get_full_repo_name; print('success')"
 
 pip install build
 python -m build
-pip install "dist/alphadia-1.6.1-py3-none-any.whl[stable]"
+pip install "dist/alphadia-1.6.2-py3-none-any.whl[stable]"
 
 # Creating the stand-alone pyinstaller folder
 pip install pyinstaller
diff --git a/release/macos/build_pkg_macos.sh b/release/macos/build_pkg_macos.sh
index 755e135a..f8254a55 100755
--- a/release/macos/build_pkg_macos.sh
+++ b/release/macos/build_pkg_macos.sh
@@ -2,7 +2,7 @@
 
 # Set up package name and version
 PACKAGE_NAME="alphadia"
-PACKAGE_VERSION="1.6.1"
+PACKAGE_VERSION="1.6.2"
 
 ARCH=$(uname -m)
 if [ "$ARCH" == "x86_64" ]; then
diff --git a/release/macos/build_zip_macos.sh b/release/macos/build_zip_macos.sh
index 9c16d9ee..886b5b24 100755
--- a/release/macos/build_zip_macos.sh
+++ b/release/macos/build_zip_macos.sh
@@ -2,7 +2,7 @@
 
 # Set up package name and version
 PACKAGE_NAME="alphadia"
-PACKAGE_VERSION="1.6.1"
+PACKAGE_VERSION="1.6.2"
 
 ARCH=$(uname -m)
 if [ "$ARCH" == "x86_64" ]; then
diff --git a/release/macos/distribution.xml b/release/macos/distribution.xml
index 1b915b2a..095ceb88 100644
--- a/release/macos/distribution.xml
+++ b/release/macos/distribution.xml
@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="utf-8" standalone="no"?>
 <installer-script minSpecVersion="1.000000">
-    <title>alphaDIA 1.6.1</title>
+    <title>alphaDIA 1.6.2</title>
     <background mime-type="image/png" file="alphadia.png" scaling="proportional"/>
     <welcome file="welcome.html" mime-type="text/html" />
     <conclusion file="conclusion.html" mime-type="text/html" />
diff --git a/release/macos/info.plist b/release/macos/info.plist
index d38fd261..2d5e4643 100644
--- a/release/macos/info.plist
+++ b/release/macos/info.plist
@@ -9,9 +9,9 @@
 	<key>CFBundleIconFile</key>
 	<string>alphadia.icns</string>
 	<key>CFBundleIdentifier</key>
-	<string>alphadia.1.6.1</string>
+	<string>alphadia.1.6.2</string>
 	<key>CFBundleShortVersionString</key>
-	<string>1.6.1</string>
+	<string>1.6.2</string>
 	<key>CFBundleInfoDictionaryVersion</key>
 	<string>6.0</string>
 	<key>CFBundleName</key>
diff --git a/release/windows/alphadia_innoinstaller.iss b/release/windows/alphadia_innoinstaller.iss
index 21b96aac..ad02b87c 100644
--- a/release/windows/alphadia_innoinstaller.iss
+++ b/release/windows/alphadia_innoinstaller.iss
@@ -2,7 +2,7 @@
 ; SEE THE DOCUMENTATION FOR DETAILS ON CREATING INNO SETUP SCRIPT FILES!
 
 #define MyAppName "AlphaDIA"
-#define MyAppVersion "1.6.1"
+#define MyAppVersion "1.6.2"
 #define MyAppPublisher "Max Planck Institute of Biochemistry, Mann Labs"
 #define MyAppURL "https://github.com/MannLabs/alphadia"
 #define MyAppExeName "alphadia-gui.exe"
@@ -25,7 +25,7 @@ LicenseFile=..\..\LICENSE.txt
 PrivilegesRequired=lowest
 PrivilegesRequiredOverridesAllowed=dialog
 OutputDir=..\..\dist
-OutputBaseFilename=alphadia-1.6.1-win-x64
+OutputBaseFilename=alphadia-1.6.2-win-x64
 SetupIconFile=..\logos\alphadia.ico
 Compression=lzma
 SolidCompression=yes
diff --git a/release/windows/build_backend.ps1 b/release/windows/build_backend.ps1
index 40ceb909..dae15414 100644
--- a/release/windows/build_backend.ps1
+++ b/release/windows/build_backend.ps1
@@ -6,7 +6,7 @@ python -c 'from huggingface_hub import get_full_repo_name; print("success")'
 
 pip install build
 python -m build
-pip install "dist/alphadia-1.6.1-py3-none-any.whl[stable]"
+pip install "dist/alphadia-1.6.2-py3-none-any.whl[stable]"
 
 # Creating the stand-alone pyinstaller folder
 pip install pyinstaller tbb


Test patch:
diff --git a/docs/alphadia/testing.rst b/docs/alphadia/testing.rst
deleted file mode 100644
index f7f02fe2..00000000
--- a/docs/alphadia/testing.rst
+++ /dev/null
@@ -1,9 +0,0 @@
-testing
-=======
-
-The testing module contains utility functions used during unit and performance tests.
-
-.. automodule:: alphadia.testing
-   :members:
-   :undoc-members:
-   :show-inheritance: