improve docs (#768)

docs/tutorials.md

@@ -1,9 +1,11 @@
 #Introduction
 
-There are a few tutorials available as [Jupyter Notebooks](https://github.com/Deltares/dfm_tools/blob/main/docs/notebooks) which are embedded on the following pages. You can [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/Deltares/dfm_tools/HEAD?urlpath=/tree/docs/notebooks) to run these notebooks interactively (loading can take a while).
+There are a few dfm_tools tutorials available as [Jupyter Notebooks](https://github.com/Deltares/dfm_tools/blob/main/docs/notebooks) which are embedded on the following pages. There are also [example scripts](https://github.com/Deltares/dfm_tools/tree/main/tests/examples) available with more specific applications. The [pdf slides](https://nbviewer.org/github/Deltares/dfm_tools/raw/main/docs/dfm_tools.pdf) contain an overview of dfm_tools information, features and examples.
 
-There are also [example scripts](https://github.com/Deltares/dfm_tools/tree/main/tests/examples) available with more specific applications.
+dfm_tools is built on top of other well documentated packages like [xarray](https://docs.xarray.dev/en/stable/getting-started-guide/quick-overview.html), [xugrid](https://deltares.github.io/xugrid/user_guide.html), [pandas](https://pandas.pydata.org/docs/getting_started/index.html), [geopandas](https://geopandas.org/en/stable/getting_started/introduction.html) and [HYDROLIB-core](https://deltares.github.io/HYDROLIB-core). Checking the `type()` of a variable will help to find the relevant documentation.
 
-More inspiration in the [pdf](https://nbviewer.org/github/Deltares/dfm_tools/raw/main/docs/dfm_tools.pdf) with dfm_tools information, features and examples
+#Pre-processing
+For pre-processing (e.g. the modelbuilder), the focus is often on data conversion. Raw data is being read with pandas, geopdandas or xarray, then processed (e.g. interpolated) and then written to model input files with xarray or HYDROLIB-core. For mesh generation, the [MeshKernelPy](https://deltares.github.io/MeshKernelPy/examples/index.html) package is used.
 
-dfm_tools uses xarray in many of its functions. If you are unfamiliar with it, please read the [xarray tutorial](https://tutorial.xarray.dev/overview/xarray-in-45-min.html). When processing FM mapoutput xugrid is used, which is a wrapper on top of xarray that supports ugrid topology. Please read the [xugrid user guide](https://deltares.github.io/xugrid/user_guide.html) if you want to know more.
+#Post-processing
+For post-processing, the main sources of information are xarray and xugrid. For instance, the function `dfmt.open_partitioned_dataset()` returns a variable of type xugrid.UgridDataset. The [xugrid user guide](https://deltares.github.io/xugrid/user_guide.html) contains many useful examples. The xugrid package in its turn wraps the underlying data as xarray datasets. This is a powerful package for lazy loading netcdf data (among others) and performing delayed operations on them. If you are unfamiliar with it, please read the [xarray in 45 minutes tutorial](https://tutorial.xarray.dev/overview/xarray-in-45-min.html).