Update README.md

Author: fs446

README.md

@@ -10,47 +10,26 @@ Jupyter notebooks can be accessed via the services
 - **static** version using **nbviewer**: https://nbviewer.org/github/spatialaudio/data-driven-audio-signal-processing-exercise/blob/dev/index.ipynb
 - **sources** (tex, ipynb) at: https://github.com/spatialaudio/data-driven-audio-signal-processing-exercise
 
-Jupyter notebooks with rendered **outputs** can be viewed at https://nbviewer.org/github/spatialaudio/data-driven-audio-signal-processing-exercise/blob/main/index.ipynb
-
 ## Versions / Tags
 
 - [v0.1](https://github.com/spatialaudio/data-driven-audio-signal-processing-exercise/releases/tag/v0.1) for winter term 2021/22, initial version
 - [v0.2](https://github.com/spatialaudio/data-driven-audio-signal-processing-exercise/releases/tag/v0.2) for winter term 2022/23
 - [v0.3](https://github.com/spatialaudio/data-driven-audio-signal-processing-exercise/releases/tag/v0.3) for winter term 2023/24, many beamer tex slides added, CI
-- TBD for winter term 2024/25
+- [v0.4](https://github.com/spatialaudio/data-driven-audio-signal-processing-exercise/releases/tag/v0.4) winter term 2024/25, smaller mods due to API changes, PCA example on exam grades, slides
+- [v0.5](https://github.com/spatialaudio/data-driven-audio-signal-processing-exercise/releases/tag/v0.5) winter term 2025/26, TBD
 
 ## Branch Conventions
 
-- the **default branch** of the repository is `dev` used for development
-- all notebook outputs in `dev` branch are cleared for convenient diff handling
-- `main` branch contains notebooks with rendered outputs, which is maintained from time to time
+- the **default branch** of the repository is `dev` and this is used for development
+- the `dev` branch contains notebooks with cleared outputs for convenient diff handling
+- the `main` branch contains notebooks with rendered outputs, which is maintained from time to time
 - do **not** rely on `main` branch as this is hard reset from time to time
 - probably in future we rename `main` to somewhat less confusing
 
-## Anaconda Environment for Local Usage
-
-The [Anaconda distribution](https://www.anaconda.com/distribution/) is a convenient solution to install a required environment, i.e. to have access to a Jupyter Notebook renderer with a Python interpreter on a personal computer. It is very likely that a very recent installation of Anaconda already delivers most of the required standard packages just using the `base` environment. It is however good practice to create a dedicated environment for each project. So, for this tutorial we might use a `myddasp` (or whatever name works for us) environment. We might consider the following install routine:
-
-- clone the repo to local machine (if not already available)
-    - `git clone [email protected]:spatialaudio/data-driven-audio-signal-processing-exercise.git` (via SSH) or
-    - `git clone https://github.com/spatialaudio/data-driven-audio-signal-processing-exercise.git` (via https) or
-    - get a zip file from current `dev` commit via https://github.com/spatialaudio/data-driven-audio-signal-processing-exercise/archive/refs/heads/dev.zip
-- get into the folder where the exercises are located, e.g. `cd my_ddasp_folder`
-- in the subfolder `.binder` the `environment.yml` can be used to create a dedicated conda `myddasp` environment as
-    - `conda env create -f environment.yml --force`
-    - (we can remove this environment with `conda env remove --name myddasp`)
-- activate this environment with `conda activate myddasp`
-- this should also have installed sound / audio related libraries using pip
-    - `pip install pyloudnorm==0.1.0`
-    - we might check this with `pip list`
-- Jupyter notebook renderer needs to know our dedicated environment:
-`python -m ipykernel install --user --name myddasp --display-name "myddasp"`
-- we might want to archive the actually installed package versions by
-    - `python -m pip list > detailed_packages_list_pip.txt` and
-    - `conda env export --no-builds > detailed_packages_list_conda.txt`
-- start a Jupyter lab environment via a local server instance by `jupyter lab`
-- start the landing page `index.ipynb` of the tutorial
-- make sure that the notebooks we want to work with are using our dedicated kernel `myddasp`
+## Python Environment
+- the `pyproject.toml` contains the project info
+- assuming we use uv for Python, packaging and environment handling a dedicated environment can be cerated with `uv sync`
+ 
 
 ## Authorship