diff --git a/.github/workflows/generate-requirements.yml b/.github/workflows/generate-requirements.yml
new file mode 100644
index 0000000000000000000000000000000000000000..f971a3d80fa79f3a7276953adec449b5987e203a
--- /dev/null
+++ b/.github/workflows/generate-requirements.yml
@@ -0,0 +1,21 @@
+name: Generate requirements.txt for readthedocs
+
+on:
+  push:
+    branches: [main]
+
+jobs:
+  generate-requirements:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v3
+      with:
+        lfs: true
+    - name: Set up Python 3.8
+      uses: actions/setup-python@v4
+      with:
+        python-version: 3.8
+    - name: Install Poetry
+      run: curl -sSL https://install.python-poetry.org | python3 -
+    - name: Export requirements.txt
+      run: poetry export --without-hashes --format=requirements.txt -o docs/requirements.txt -E docs -E notebook
diff --git a/README.md b/README.md
index 005fc9310b247e6f59b335cafff7ead9e6c626e2..b15c4d82543650db160e4f90d1180299eabd8aa3 100644
--- a/README.md
+++ b/README.md
@@ -1,11 +1,11 @@
 # sed
 [![Documentation Status](https://readthedocs.org/projects/sed/badge/?version=latest)](https://sed.readthedocs.io/en/latest/?badge=latest)
-![](https://github.com/OpenCOMPES/sed/actions/workflows/pylint.yml/badge.svg?branch=github-tokens-and-coverage)
-![](https://github.com/OpenCOMPES/sed/actions/workflows/pytest.yml/badge.svg?branch=github-tokens-and-coverage)
+![](https://github.com/OpenCOMPES/sed/actions/workflows/pylint.yml/badge.svg?branch=main)
+![](https://github.com/OpenCOMPES/sed/actions/workflows/pytest.yml/badge.svg?branch=main)
 ![](https://img.shields.io/pypi/pyversions/sedprocessor)
 ![](https://img.shields.io/pypi/l/sedprocessor)
 ![](https://img.shields.io/pypi/v/sedprocessor)
-[![Coverage Status](https://coveralls.io/repos/github/OpenCOMPES/sed/badge.svg?branch=github-tokens-and-coverage)](https://coveralls.io/github/OpenCOMPES/sed?branch=github-tokens-and-coverage)
+[![Coverage Status](https://coveralls.io/repos/github/OpenCOMPES/sed/badge.svg?branch=main&kill_cache=1)](https://coveralls.io/github/OpenCOMPES/sed?branch=main)
 
 
 Single Event Data Frame Processor: Backend to handle photoelectron resolved datastreams
diff --git a/docs/index.rst b/docs/index.rst
index 8c718ab1a5865be0e416835a3cfd556b5cc37c31..c73bb857f1a452ae559fdd5cebdc6a8eb8f15c65 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -2,7 +2,7 @@ Single-Event DataFrame (SED) documentation
 ================================================
 
 .. toctree::
-   :maxdepth: 3
+   :maxdepth: 1
    :numbered:
    :caption: SED Core Modules
 
diff --git a/docs/requirements.txt b/docs/requirements.txt
index db41dd80102ee64841b3099bd253b28a577ce134..22a0694b7bde66d06d396e4383a2b6a940fdf246 100644
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -1,58 +1,222 @@
-alabaster==0.7.12; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7"
-babel==2.9.1; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7"
-certifi==2021.10.8; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6"
-charset-normalizer==2.0.12; python_full_version >= "3.6.0" and python_version >= "3.6"
-cloudpickle==2.0.0; python_version >= "3.7"
-colorama==0.4.4; python_version >= "3.7" and python_full_version < "3.0.0" and platform_system == "Windows" and sys_platform == "win32" and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6") or python_full_version >= "3.5.0" and platform_system == "Windows" and sys_platform == "win32" and python_version >= "3.7" and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6")
-cramjam==2.5.0; python_version >= "3.6"
-cycler==0.11.0; python_version >= "3.7"
-dask==2021.12.0; python_version >= "3.7"
-docutils==0.17.1; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7"
-fastparquet==0.8.0; python_version >= "3.6"
-fonttools==4.30.0; python_version >= "3.7"
-fsspec==2022.2.0; python_version >= "3.7"
-h5py==3.6.0; python_version >= "3.7"
-idna==3.3; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6"
-imagesize==1.3.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7"
-importlib-metadata==4.11.3; python_version < "3.10" and python_version >= "3.7" and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6")
-jinja2==3.0.3; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7"
-kiwisolver==1.4.0; python_version >= "3.7"
-llvmlite==0.38.0; python_version >= "3.7" and python_version < "3.11"
-locket==0.2.1; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7"
-markupsafe==2.1.1; python_version >= "3.7"
-matplotlib==3.5.1; python_version >= "3.7"
-numba==0.55.1; python_version >= "3.7" and python_version < "3.11"
-numpy==1.21.5; python_version >= "3.7" and python_version < "3.11"
-packaging==21.3; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7"
-pandas==1.4.1; python_version >= "3.8"
-partd==1.2.0; python_version >= "3.7"
-pillow==9.0.1; python_version >= "3.7"
-psutil==5.9.0; (python_version >= "2.6" and python_full_version < "3.0.0") or (python_full_version >= "3.4.0")
-pyarrow==7.0.0; python_version >= "3.7"
-pygments==2.11.2; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7"
-pyparsing==3.0.7; python_version >= "3.7"
-python-dateutil==2.8.2; python_version >= "3.8" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.8"
-pytz==2021.3; python_version >= "3.8" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.8"
-pyyaml==6.0; python_version >= "3.7"
-requests==2.27.1; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7"
-setuptools-scm==6.4.2; python_version >= "3.7"
-six==1.16.0; python_version >= "3.8" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.8"
-snowballstemmer==2.2.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7"
-sphinx-autodoc-typehints==1.17.0; python_version >= "3.7"
-sphinx-rtd-theme==1.0.0; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.4.0")
-sphinx==4.4.0; python_version >= "3.6"
-sphinxcontrib-applehelp==1.0.2; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7"
-sphinxcontrib-devhelp==1.0.2; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7"
-sphinxcontrib-htmlhelp==2.0.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7"
-sphinxcontrib-jsmath==1.0.1; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7"
-sphinxcontrib-qthelp==1.0.3; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7"
-sphinxcontrib-serializinghtml==1.1.5; python_version >= "3.6" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7")
-sphinxcontrib-websupport==1.2.4; python_version >= "3.7" and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6")
-threadpoolctl==3.1.0; python_version >= "3.6"
-tomli==2.0.1; python_version >= "3.7"
-tomlkit==0.10.0; python_version >= "3.6" and python_version < "4.0"
-toolz==0.11.2; python_version >= "3.7"
-tqdm==4.63.0; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.4.0")
-urllib3==1.26.8; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version < "4" and python_version >= "3.6"
-xarray==0.20.2; python_version >= "3.7"
-zipp==3.7.0; python_version < "3.10" and python_version >= "3.7"
+alabaster==0.7.13 ; python_version >= "3.8" and python_version < "3.11"
+anyio==3.7.1 ; python_version >= "3.8" and python_version < "3.11"
+appnope==0.1.3 ; python_version >= "3.8" and python_version < "3.11" and platform_system == "Darwin" or python_version >= "3.8" and python_version < "3.11" and sys_platform == "darwin"
+argon2-cffi-bindings==21.2.0 ; python_version >= "3.8" and python_version < "3.11"
+argon2-cffi==21.3.0 ; python_version >= "3.8" and python_version < "3.11"
+arrow==1.2.3 ; python_version >= "3.8" and python_version < "3.11"
+asciitree==0.3.3 ; python_version >= "3.8" and python_version < "3.11" and platform_machine == "x86_64" or python_version >= "3.8" and python_version < "3.11" and platform_machine == "AMD64"
+ase==3.22.1 ; python_version >= "3.8" and python_version < "3.11"
+asteval==0.9.31 ; python_version >= "3.8" and python_version < "3.11"
+astropy==5.2.2 ; python_version >= "3.8" and python_version < "3.11"
+asttokens==2.2.1 ; python_version >= "3.8" and python_version < "3.11"
+attrs==23.1.0 ; python_version >= "3.8" and python_version < "3.11"
+babel==2.12.1 ; python_version >= "3.8" and python_version < "3.11"
+backcall==0.2.0 ; python_version >= "3.8" and python_version < "3.11"
+beautifulsoup4==4.12.2 ; python_version >= "3.8" and python_version < "3.11"
+bleach==6.0.0 ; python_version >= "3.8" and python_version < "3.11"
+bokeh==2.4.3 ; python_version >= "3.8" and python_version < "3.11"
+cattrs==23.1.2 ; python_version >= "3.8" and python_version < "3.11"
+certifi==2023.7.22 ; python_version >= "3.8" and python_version < "3.11"
+cffi==1.15.1 ; python_version >= "3.8" and python_version < "3.11"
+charset-normalizer==3.2.0 ; python_version >= "3.8" and python_version < "3.11"
+click==8.1.6 ; python_version >= "3.8" and python_version < "3.11"
+cloudpickle==2.2.1 ; python_version >= "3.8" and python_version < "3.11"
+colorama==0.4.6 ; python_version >= "3.8" and python_version < "3.11" and platform_system == "Windows" or python_version >= "3.8" and python_version < "3.11" and sys_platform == "win32"
+comm==0.1.4 ; python_version >= "3.8" and python_version < "3.11"
+commonmark==0.9.1 ; python_version >= "3.8" and python_version < "3.11"
+contourpy==1.1.0 ; python_version >= "3.8" and python_version < "3.11"
+cramjam==2.7.0 ; python_version >= "3.8" and python_version < "3.11"
+cycler==0.11.0 ; python_version >= "3.8" and python_version < "3.11"
+dask==2022.12.1 ; python_version >= "3.8" and python_version < "3.11"
+dask[array]==2022.12.1 ; python_version >= "3.8" and python_version < "3.11"
+debugpy==1.6.7.post1 ; python_version >= "3.8" and python_version < "3.11"
+decorator==5.1.1 ; python_version >= "3.8" and python_version < "3.11"
+defusedxml==0.7.1 ; python_version >= "3.8" and python_version < "3.11"
+diffpy-structure==3.1.0 ; python_version >= "3.8" and python_version < "3.11"
+diffsims==0.5.2 ; python_version >= "3.8" and python_version < "3.11"
+dill==0.3.7 ; python_version >= "3.8" and python_version < "3.11"
+docutils==0.18.1 ; python_version >= "3.8" and python_version < "3.11"
+entrypoints==0.4 ; python_version >= "3.8" and python_version < "3.11"
+exceptiongroup==1.1.2 ; python_version >= "3.8" and python_version < "3.11"
+executing==1.2.0 ; python_version >= "3.8" and python_version < "3.11"
+fabio==2023.6.0 ; python_version >= "3.8" and python_version < "3.11"
+fastdtw==0.3.4 ; python_version >= "3.8" and python_version < "3.11"
+fasteners==0.18 ; python_version >= "3.8" and python_version < "3.11" and platform_machine == "x86_64" or python_version >= "3.8" and python_version < "3.11" and platform_machine == "AMD64"
+fastjsonschema==2.18.0 ; python_version >= "3.8" and python_version < "3.11"
+fastparquet==0.8.3 ; python_version >= "3.8" and python_version < "3.11"
+flatdict==4.0.1 ; python_version >= "3.8" and python_version < "3.11"
+fonttools==4.42.0 ; python_version >= "3.8" and python_version < "3.11"
+fqdn==1.5.1 ; python_version >= "3.8" and python_version < "3.11"
+fsspec==2023.6.0 ; python_version >= "3.8" and python_version < "3.11"
+furo==2023.7.26 ; python_version >= "3.8" and python_version < "3.11"
+future==0.18.3 ; python_version >= "3.8" and python_version < "3.11"
+gitdb==4.0.10 ; python_version >= "3.8" and python_version < "3.11"
+gitpython==3.1.32 ; python_version >= "3.8" and python_version < "3.11"
+h5grove==1.3.0 ; python_version >= "3.8" and python_version < "3.11"
+h5py==3.9.0 ; python_version >= "3.8" and python_version < "3.11"
+hyperspy==1.7.5 ; python_version >= "3.8" and python_version < "3.11"
+idna==3.4 ; python_version >= "3.8" and python_version < "3.11"
+ifes-apt-tc-data-modeling==0.0.9 ; python_version >= "3.8" and python_version < "3.11"
+imageio==2.27.0 ; python_version >= "3.8" and python_version < "3.11"
+imagesize==1.4.1 ; python_version >= "3.8" and python_version < "3.11"
+importlib-metadata==6.8.0 ; python_version >= "3.8" and python_version < "3.11"
+importlib-resources==6.0.1 ; python_version >= "3.8" and python_version < "3.10"
+ipykernel==6.25.1 ; python_version >= "3.8" and python_version < "3.11"
+ipykernel[notebook]==6.25.1 ; python_version >= "3.8" and python_version < "3.11"
+ipympl==0.9.3 ; python_version >= "3.8" and python_version < "3.11"
+ipyparallel==8.6.1 ; python_version >= "3.8" and python_version < "3.11"
+ipython-genutils==0.2.0 ; python_version >= "3.8" and python_version < "3.11"
+ipython==8.12.2 ; python_version >= "3.8" and python_version < "3.11"
+ipywidgets==7.8.0 ; python_version >= "3.8" and python_version < "3.11"
+isoduration==20.11.0 ; python_version >= "3.8" and python_version < "3.11"
+jedi==0.19.0 ; python_version >= "3.8" and python_version < "3.11"
+jinja2==3.1.2 ; python_version >= "3.8" and python_version < "3.11"
+joblib==1.3.2 ; python_version >= "3.8" and python_version < "3.11"
+json5==0.9.14 ; python_version >= "3.8" and python_version < "3.11"
+jsonpointer==2.4 ; python_version >= "3.8" and python_version < "3.11"
+jsonschema-specifications==2023.7.1 ; python_version >= "3.8" and python_version < "3.11"
+jsonschema==4.19.0 ; python_version >= "3.8" and python_version < "3.11"
+jsonschema[format-nongpl]==4.19.0 ; python_version >= "3.8" and python_version < "3.11"
+jupyter-client==8.3.0 ; python_version >= "3.8" and python_version < "3.11"
+jupyter-console==6.6.3 ; python_version >= "3.8" and python_version < "3.11"
+jupyter-core==5.3.1 ; python_version >= "3.8" and python_version < "3.11"
+jupyter-events==0.7.0 ; python_version >= "3.8" and python_version < "3.11"
+jupyter-server-terminals==0.4.4 ; python_version >= "3.8" and python_version < "3.11"
+jupyter-server==2.7.0 ; python_version >= "3.8" and python_version < "3.11"
+jupyter[notebook]==1.0.0 ; python_version >= "3.8" and python_version < "3.11"
+jupyterlab-h5web==10.0.0 ; python_version >= "3.8" and python_version < "3.11"
+jupyterlab-pygments==0.2.2 ; python_version >= "3.8" and python_version < "3.11"
+jupyterlab-server==2.24.0 ; python_version >= "3.8" and python_version < "3.11"
+jupyterlab-widgets==1.1.5 ; python_version >= "3.8" and python_version < "3.11"
+jupyterlab==3.5.3 ; python_version >= "3.8" and python_version < "3.11"
+kikuchipy==0.8.7 ; python_version >= "3.8" and python_version < "3.11"
+kiwisolver==1.4.4 ; python_version >= "3.8" and python_version < "3.11"
+lark==1.1.7 ; python_version >= "3.8" and python_version < "3.11"
+lazy-loader==0.3 ; python_version >= "3.8" and python_version < "3.11"
+llvmlite==0.38.1 ; python_version >= "3.8" and python_version < "3.11"
+lmfit==1.2.2 ; python_version >= "3.8" and python_version < "3.11"
+locket==1.0.0 ; python_version >= "3.8" and python_version < "3.11"
+markupsafe==2.1.3 ; python_version >= "3.8" and python_version < "3.11"
+matplotlib-inline==0.1.6 ; python_version >= "3.8" and python_version < "3.11"
+matplotlib-scalebar==0.8.1 ; python_version >= "3.8" and python_version < "3.11"
+matplotlib==3.7.2 ; python_version >= "3.8" and python_version < "3.11"
+mistune==3.0.1 ; python_version >= "3.8" and python_version < "3.11"
+mpmath==1.3.0 ; python_version >= "3.8" and python_version < "3.11"
+natsort==8.4.0 ; python_version >= "3.8" and python_version < "3.11"
+nbclassic==1.0.0 ; python_version >= "3.8" and python_version < "3.11"
+nbclient==0.8.0 ; python_version >= "3.8" and python_version < "3.11"
+nbconvert==7.7.3 ; python_version >= "3.8" and python_version < "3.11"
+nbformat==5.9.2 ; python_version >= "3.8" and python_version < "3.11"
+nest-asyncio==1.5.7 ; python_version >= "3.8" and python_version < "3.11"
+networkx==3.1 ; python_version >= "3.8" and python_version < "3.11"
+notebook-shim==0.2.3 ; python_version >= "3.8" and python_version < "3.11"
+notebook==6.5.4 ; python_version >= "3.8" and python_version < "3.11"
+numba==0.55.2 ; python_version >= "3.8" and python_version < "3.11"
+numcodecs==0.11.0 ; python_version >= "3.8" and python_version < "3.11" and platform_machine == "x86_64" or python_version >= "3.8" and python_version < "3.11" and platform_machine == "AMD64"
+numexpr==2.8.5 ; python_version >= "3.8" and python_version < "3.11"
+numpy-quaternion==2022.4.3 ; python_version >= "3.8" and python_version < "3.11"
+numpy==1.21.6 ; python_version < "3.11" and python_version >= "3.8"
+opencv-python==4.8.0.74 ; python_version >= "3.8" and python_version < "3.11"
+orix==0.11.1 ; python_version >= "3.8" and python_version < "3.11"
+orjson==3.9.4 ; python_version >= "3.8" and python_version < "3.11"
+overrides==7.4.0 ; python_version >= "3.8" and python_version < "3.11"
+packaging==23.1 ; python_version >= "3.8" and python_version < "3.11"
+pandas==1.5.3 ; python_version >= "3.8" and python_version < "3.11"
+pandocfilters==1.5.0 ; python_version >= "3.8" and python_version < "3.11"
+parso==0.8.3 ; python_version >= "3.8" and python_version < "3.11"
+partd==1.4.0 ; python_version >= "3.8" and python_version < "3.11"
+pexpect==4.8.0 ; python_version >= "3.8" and python_version < "3.11" and sys_platform != "win32"
+photutils==1.8.0 ; python_version >= "3.8" and python_version < "3.11"
+pickleshare==0.7.5 ; python_version >= "3.8" and python_version < "3.11"
+pillow==10.0.0 ; python_version >= "3.8" and python_version < "3.11"
+pint==0.21.1 ; python_version >= "3.8" and python_version < "3.11"
+pkgutil-resolve-name==1.3.10 ; python_version >= "3.8" and python_version < "3.9"
+platformdirs==3.10.0 ; python_version >= "3.8" and python_version < "3.11"
+pooch==1.7.0 ; python_version >= "3.8" and python_version < "3.11"
+prettytable==3.8.0 ; python_version >= "3.8" and python_version < "3.11"
+prometheus-client==0.17.1 ; python_version >= "3.8" and python_version < "3.11"
+prompt-toolkit==3.0.39 ; python_version >= "3.8" and python_version < "3.11"
+psutil==5.9.5 ; python_version >= "3.8" and python_version < "3.11"
+ptyprocess==0.7.0 ; python_version >= "3.8" and python_version < "3.11" and sys_platform != "win32" or python_version >= "3.8" and python_version < "3.11" and os_name != "nt"
+pure-eval==0.2.2 ; python_version >= "3.8" and python_version < "3.11"
+pycifrw==4.4.5 ; python_version >= "3.8" and python_version < "3.11"
+pycparser==2.21 ; python_version >= "3.8" and python_version < "3.11"
+pyerfa==2.0.0.3 ; python_version >= "3.8" and python_version < "3.11"
+pyfai==2023.5.0 ; python_version >= "3.8" and python_version < "3.11"
+pygments==2.16.1 ; python_version >= "3.8" and python_version < "3.11"
+pynxtools==0.0.2 ; python_version >= "3.8" and python_version < "3.11"
+pyparsing==3.0.9 ; python_version >= "3.8" and python_version < "3.11"
+python-dateutil==2.8.2 ; python_version >= "3.8" and python_version < "3.11"
+python-json-logger==2.0.7 ; python_version >= "3.8" and python_version < "3.11"
+pytz==2023.3 ; python_version >= "3.8" and python_version < "3.11"
+pywavelets==1.4.1 ; python_version >= "3.8" and python_version < "3.11"
+pywin32==306 ; sys_platform == "win32" and platform_python_implementation != "PyPy" and python_version >= "3.8" and python_version < "3.11"
+pywinpty==2.0.11 ; python_version >= "3.8" and python_version < "3.11" and os_name == "nt"
+pyxem==0.15.1 ; python_version >= "3.8" and python_version < "3.11"
+pyyaml==6.0.1 ; python_version >= "3.8" and python_version < "3.11"
+pyzmq==25.1.1 ; python_version >= "3.8" and python_version < "3.11"
+qtconsole==5.4.3 ; python_version >= "3.8" and python_version < "3.11"
+qtpy==2.3.1 ; python_version >= "3.8" and python_version < "3.11"
+radioactivedecay==0.4.20 ; python_version >= "3.8" and python_version < "3.11"
+recommonmark==0.7.1 ; python_version >= "3.8" and python_version < "3.11"
+referencing==0.30.2 ; python_version >= "3.8" and python_version < "3.11"
+requests-cache==1.1.0 ; python_version >= "3.8" and python_version < "3.11"
+requests==2.31.0 ; python_version >= "3.8" and python_version < "3.11"
+rfc3339-validator==0.1.4 ; python_version >= "3.8" and python_version < "3.11"
+rfc3986-validator==0.1.1 ; python_version >= "3.8" and python_version < "3.11"
+rpds-py==0.9.2 ; python_version >= "3.8" and python_version < "3.11"
+scikit-image==0.21.0 ; python_version >= "3.8" and python_version < "3.11"
+scikit-learn==1.3.0 ; python_version >= "3.8" and python_version < "3.11"
+scipy==1.10.1 ; python_version >= "3.8" and python_version < "3.11"
+send2trash==1.8.2 ; python_version >= "3.8" and python_version < "3.11"
+setuptools==68.0.0 ; python_version >= "3.8" and python_version < "3.11"
+silx==1.1.2 ; python_version >= "3.8" and python_version < "3.11"
+six==1.16.0 ; python_version >= "3.8" and python_version < "3.11"
+smmap==5.0.0 ; python_version >= "3.8" and python_version < "3.11"
+sniffio==1.3.0 ; python_version >= "3.8" and python_version < "3.11"
+snowballstemmer==2.2.0 ; python_version >= "3.8" and python_version < "3.11"
+soupsieve==2.4.1 ; python_version >= "3.8" and python_version < "3.11"
+sparse==0.14.0 ; python_version >= "3.8" and python_version < "3.11"
+sphinx-autodoc-typehints[docs]==1.24.0 ; python_version >= "3.8" and python_version < "3.11"
+sphinx-basic-ng==1.0.0b2 ; python_version >= "3.8" and python_version < "3.11"
+sphinx-rtd-theme==1.3.0 ; python_version >= "3.8" and python_version < "3.11"
+sphinx-rtd-theme[docs]==1.3.0 ; python_version >= "3.8" and python_version < "3.11"
+sphinx==7.1.2 ; python_version >= "3.8" and python_version < "3.11"
+sphinx[docs]==7.1.2 ; python_version >= "3.8" and python_version < "3.11"
+sphinxcontrib-applehelp==1.0.4 ; python_version >= "3.8" and python_version < "3.11"
+sphinxcontrib-devhelp==1.0.2 ; python_version >= "3.8" and python_version < "3.11"
+sphinxcontrib-htmlhelp==2.0.1 ; python_version >= "3.8" and python_version < "3.11"
+sphinxcontrib-jquery==4.1 ; python_version >= "3.8" and python_version < "3.11"
+sphinxcontrib-jsmath==1.0.1 ; python_version >= "3.8" and python_version < "3.11"
+sphinxcontrib-qthelp==1.0.3 ; python_version >= "3.8" and python_version < "3.11"
+sphinxcontrib-serializinghtml==1.1.5 ; python_version >= "3.8" and python_version < "3.11"
+sphinxcontrib-websupport==1.2.4 ; python_version >= "3.8" and python_version < "3.11"
+stack-data==0.6.2 ; python_version >= "3.8" and python_version < "3.11"
+symmetrize==0.5.5 ; python_version >= "3.8" and python_version < "3.11"
+sympy==1.12 ; python_version >= "3.8" and python_version < "3.11"
+terminado==0.17.1 ; python_version >= "3.8" and python_version < "3.11"
+threadpoolctl==3.2.0 ; python_version >= "3.8" and python_version < "3.11"
+tifffile==2022.10.10 ; python_version >= "3.8" and python_version < "3.11"
+tinycss2==1.2.1 ; python_version >= "3.8" and python_version < "3.11"
+tomli==2.0.1 ; python_version >= "3.8" and python_version < "3.11"
+tomlkit[docs]==0.12.1 ; python_version >= "3.8" and python_version < "3.11"
+toolz==0.12.0 ; python_version >= "3.8" and python_version < "3.11"
+tornado==6.3.2 ; python_version >= "3.8" and python_version < "3.11"
+tqdm==4.66.1 ; python_version >= "3.8" and python_version < "3.11"
+traitlets==5.9.0 ; python_version >= "3.8" and python_version < "3.11"
+traits==6.4.2 ; python_version >= "3.8" and python_version < "3.11"
+transforms3d==0.4.1 ; python_version >= "3.8" and python_version < "3.11"
+typing-extensions==4.7.1 ; python_version >= "3.8" and python_version < "3.11"
+uncertainties==3.1.7 ; python_version >= "3.8" and python_version < "3.11"
+uri-template==1.3.0 ; python_version >= "3.8" and python_version < "3.11"
+url-normalize==1.4.3 ; python_version >= "3.8" and python_version < "3.11"
+urllib3==2.0.4 ; python_version >= "3.8" and python_version < "3.11"
+wcwidth==0.2.6 ; python_version >= "3.8" and python_version < "3.11"
+webcolors==1.13 ; python_version >= "3.8" and python_version < "3.11"
+webencodings==0.5.1 ; python_version >= "3.8" and python_version < "3.11"
+websocket-client==1.6.1 ; python_version >= "3.8" and python_version < "3.11"
+widgetsnbextension==3.6.5 ; python_version >= "3.8" and python_version < "3.11"
+xarray==0.20.2 ; python_version >= "3.8" and python_version < "3.11"
+zarr==2.16.0 ; python_version >= "3.8" and python_version < "3.11" and platform_machine == "x86_64" or python_version >= "3.8" and python_version < "3.11" and platform_machine == "AMD64"
+zipfile37==0.1.3 ; python_version >= "3.8" and python_version < "3.11"
+zipp==3.16.2 ; python_version >= "3.8" and python_version < "3.11"
diff --git a/docs/sed/config.rst b/docs/sed/config.rst
index 2864d2d4563a1990ebcc2ff304795258b08348a2..c5457fe0db34d6cab2ca504a9f45691b121ecc04 100644
--- a/docs/sed/config.rst
+++ b/docs/sed/config.rst
@@ -20,8 +20,20 @@ API
 
 .. _example_config:
 
-Example configuration file
+Default configuration settings
 ***************************************************
 
-.. literalinclude:: ../../tests/data/config/config.yaml
+.. literalinclude:: ../../sed/config/default.yaml
+   :language: yaml
+
+Example configuration file for mpes (METIS momentum microscope at FHI-Berlin)
+*********************************************************************************
+
+.. literalinclude:: ../../sed/config/mpes_example_config.yaml
+   :language: yaml
+
+Example configuration file for flash (HEXTOF momentum microscope at FLASH, Desy)
+*********************************************************************************
+
+.. literalinclude:: ../../sed/config/flash_example_config.yaml
    :language: yaml
diff --git a/docs/sed/loader.rst b/docs/sed/loader.rst
index 4bf3ba5e305ce1c769197a7f615e5ac0e4647f9a..59cc26edba4dc46d4b739ff1ca144a8bfe780ee1 100644
--- a/docs/sed/loader.rst
+++ b/docs/sed/loader.rst
@@ -25,6 +25,12 @@ MpesLoader
    :members:
    :undoc-members:
 
+FlashLoader
+###################################################
+.. automodule:: sed.loader.flash.loader
+   :members:
+   :undoc-members:
+
 Utilities
 ###################################################
 .. automodule:: sed.loader.utils
diff --git a/poetry.lock b/poetry.lock
index 6a3cd4482702e566c061c6d515e0b4d8e3f035b5..132c29254cc64e3517620785c22fbbd848bf63b3 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -14,25 +14,25 @@ files = [
 
 [[package]]
 name = "anyio"
-version = "3.7.1"
+version = "4.0.0"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 category = "main"
-optional = false
-python-versions = ">=3.7"
+optional = true
+python-versions = ">=3.8"
 files = [
-    {file = "anyio-3.7.1-py3-none-any.whl", hash = "sha256:91dee416e570e92c64041bd18b900d1d6fa78dff7048769ce5ac5ddad004fbb5"},
-    {file = "anyio-3.7.1.tar.gz", hash = "sha256:44a3c9aba0f5defa43261a8b3efb97891f2bd7d804e0e1f56419befa1adfc780"},
+    {file = "anyio-4.0.0-py3-none-any.whl", hash = "sha256:cfdb2b588b9fc25ede96d8db56ed50848b0b649dca3dd1df0b11f683bb9e0b5f"},
+    {file = "anyio-4.0.0.tar.gz", hash = "sha256:f7ed51751b2c2add651e5747c891b47e26d2a21be5d32d9311dfe9692f3e5d7a"},
 ]
 
 [package.dependencies]
-exceptiongroup = {version = "*", markers = "python_version < \"3.11\""}
+exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
 idna = ">=2.8"
 sniffio = ">=1.1"
 
 [package.extras]
-doc = ["Sphinx", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme (>=1.2.2)", "sphinxcontrib-jquery"]
-test = ["anyio[trio]", "coverage[toml] (>=4.5)", "hypothesis (>=4.0)", "mock (>=4)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
-trio = ["trio (<0.22)"]
+doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
+trio = ["trio (>=0.22)"]
 
 [[package]]
 name = "appnope"
@@ -48,30 +48,31 @@ files = [
 
 [[package]]
 name = "argon2-cffi"
-version = "21.3.0"
-description = "The secure Argon2 password hashing algorithm."
+version = "23.1.0"
+description = "Argon2 for Python"
 category = "main"
-optional = false
-python-versions = ">=3.6"
+optional = true
+python-versions = ">=3.7"
 files = [
-    {file = "argon2-cffi-21.3.0.tar.gz", hash = "sha256:d384164d944190a7dd7ef22c6aa3ff197da12962bd04b17f64d4e93d934dba5b"},
-    {file = "argon2_cffi-21.3.0-py3-none-any.whl", hash = "sha256:8c976986f2c5c0e5000919e6de187906cfd81fb1c72bf9d88c01177e77da7f80"},
+    {file = "argon2_cffi-23.1.0-py3-none-any.whl", hash = "sha256:c670642b78ba29641818ab2e68bd4e6a78ba53b7eff7b4c3815ae16abf91c7ea"},
+    {file = "argon2_cffi-23.1.0.tar.gz", hash = "sha256:879c3e79a2729ce768ebb7d36d4609e3a78a4ca2ec3a9f12286ca057e3d0db08"},
 ]
 
 [package.dependencies]
 argon2-cffi-bindings = "*"
 
 [package.extras]
-dev = ["cogapp", "coverage[toml] (>=5.0.2)", "furo", "hypothesis", "pre-commit", "pytest", "sphinx", "sphinx-notfound-page", "tomli"]
-docs = ["furo", "sphinx", "sphinx-notfound-page"]
-tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pytest"]
+dev = ["argon2-cffi[tests,typing]", "tox (>4)"]
+docs = ["furo", "myst-parser", "sphinx", "sphinx-copybutton", "sphinx-notfound-page"]
+tests = ["hypothesis", "pytest"]
+typing = ["mypy"]
 
 [[package]]
 name = "argon2-cffi-bindings"
 version = "21.2.0"
 description = "Low-level CFFI bindings for Argon2"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.6"
 files = [
     {file = "argon2-cffi-bindings-21.2.0.tar.gz", hash = "sha256:bb89ceffa6c791807d1305ceb77dbfacc5aa499891d2c55661c6459651fc39e3"},
@@ -106,18 +107,23 @@ tests = ["pytest"]
 
 [[package]]
 name = "arrow"
-version = "1.2.3"
+version = "1.3.0"
 description = "Better dates & times for Python"
 category = "main"
-optional = false
-python-versions = ">=3.6"
+optional = true
+python-versions = ">=3.8"
 files = [
-    {file = "arrow-1.2.3-py3-none-any.whl", hash = "sha256:5a49ab92e3b7b71d96cd6bfcc4df14efefc9dfa96ea19045815914a6ab6b1fe2"},
-    {file = "arrow-1.2.3.tar.gz", hash = "sha256:3934b30ca1b9f292376d9db15b19446088d12ec58629bc3f0da28fd55fb633a1"},
+    {file = "arrow-1.3.0-py3-none-any.whl", hash = "sha256:c728b120ebc00eb84e01882a6f5e7927a53960aa990ce7dd2b10f39005a67f80"},
+    {file = "arrow-1.3.0.tar.gz", hash = "sha256:d4540617648cb5f895730f1ad8c82a65f2dad0166f57b75f3ca54759c4d67a85"},
 ]
 
 [package.dependencies]
 python-dateutil = ">=2.7.0"
+types-python-dateutil = ">=2.8.10"
+
+[package.extras]
+doc = ["doc8", "sphinx (>=7.0.0)", "sphinx-autobuild", "sphinx-autodoc-typehints", "sphinx_rtd_theme (>=1.3.0)"]
+test = ["dateparser (>=1.0.0,<2.0.0)", "pre-commit", "pytest", "pytest-cov", "pytest-mock", "pytz (==2021.1)", "simplejson (>=3.0.0,<4.0.0)"]
 
 [[package]]
 name = "asciitree"
@@ -225,22 +231,37 @@ test-all = ["coverage[toml]", "ipython (>=4.2)", "objgraph", "pytest (>=7.0)", "
 
 [[package]]
 name = "asttokens"
-version = "2.2.1"
+version = "2.4.0"
 description = "Annotate AST trees with source code positions"
 category = "main"
 optional = false
 python-versions = "*"
 files = [
-    {file = "asttokens-2.2.1-py2.py3-none-any.whl", hash = "sha256:6b0ac9e93fb0335014d382b8fa9b3afa7df546984258005da0b9e7095b3deb1c"},
-    {file = "asttokens-2.2.1.tar.gz", hash = "sha256:4622110b2a6f30b77e1473affaa97e711bc2f07d3f10848420ff1898edbe94f3"},
+    {file = "asttokens-2.4.0-py2.py3-none-any.whl", hash = "sha256:cf8fc9e61a86461aa9fb161a14a0841a03c405fa829ac6b202670b3495d2ce69"},
+    {file = "asttokens-2.4.0.tar.gz", hash = "sha256:2e0171b991b2c959acc6c49318049236844a5da1d65ba2672c4880c1c894834e"},
 ]
 
 [package.dependencies]
-six = "*"
+six = ">=1.12.0"
 
 [package.extras]
 test = ["astroid", "pytest"]
 
+[[package]]
+name = "async-lru"
+version = "2.0.4"
+description = "Simple LRU cache for asyncio"
+category = "main"
+optional = true
+python-versions = ">=3.8"
+files = [
+    {file = "async-lru-2.0.4.tar.gz", hash = "sha256:b8a59a5df60805ff63220b2a0c5b5393da5521b113cd5465a44eb037d81a5627"},
+    {file = "async_lru-2.0.4-py3-none-any.whl", hash = "sha256:ff02944ce3c288c5be660c42dbcca0742b32c3b279d6dceda655190240b99224"},
+]
+
+[package.dependencies]
+typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.11\""}
+
 [[package]]
 name = "attrs"
 version = "23.1.0"
@@ -262,19 +283,22 @@ tests-no-zope = ["cloudpickle", "hypothesis", "mypy (>=1.1.1)", "pympler", "pyte
 
 [[package]]
 name = "babel"
-version = "2.12.1"
+version = "2.13.0"
 description = "Internationalization utilities"
 category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "Babel-2.12.1-py3-none-any.whl", hash = "sha256:b4246fb7677d3b98f501a39d43396d3cafdc8eadb045f4a31be01863f655c610"},
-    {file = "Babel-2.12.1.tar.gz", hash = "sha256:cc2d99999cd01d44420ae725a21c9e3711b3aadc7976d6147f622d8581963455"},
+    {file = "Babel-2.13.0-py3-none-any.whl", hash = "sha256:fbfcae1575ff78e26c7449136f1abbefc3c13ce542eeb13d43d50d8b047216ec"},
+    {file = "Babel-2.13.0.tar.gz", hash = "sha256:04c3e2d28d2b7681644508f836be388ae49e0cfe91465095340395b60d00f210"},
 ]
 
 [package.dependencies]
 pytz = {version = ">=2015.7", markers = "python_version < \"3.9\""}
 
+[package.extras]
+dev = ["freezegun (>=1.0,<2.0)", "pytest (>=6.0)", "pytest-cov"]
+
 [[package]]
 name = "backcall"
 version = "0.2.0"
@@ -287,12 +311,41 @@ files = [
     {file = "backcall-0.2.0.tar.gz", hash = "sha256:5cbdbf27be5e7cfadb448baf0aa95508f91f2bbc6c6437cd9cd06e2a4c215e1e"},
 ]
 
+[[package]]
+name = "backports-zoneinfo"
+version = "0.2.1"
+description = "Backport of the standard library zoneinfo module"
+category = "main"
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "backports.zoneinfo-0.2.1-cp36-cp36m-macosx_10_14_x86_64.whl", hash = "sha256:da6013fd84a690242c310d77ddb8441a559e9cb3d3d59ebac9aca1a57b2e18bc"},
+    {file = "backports.zoneinfo-0.2.1-cp36-cp36m-manylinux1_i686.whl", hash = "sha256:89a48c0d158a3cc3f654da4c2de1ceba85263fafb861b98b59040a5086259722"},
+    {file = "backports.zoneinfo-0.2.1-cp36-cp36m-manylinux1_x86_64.whl", hash = "sha256:1c5742112073a563c81f786e77514969acb58649bcdf6cdf0b4ed31a348d4546"},
+    {file = "backports.zoneinfo-0.2.1-cp36-cp36m-win32.whl", hash = "sha256:e8236383a20872c0cdf5a62b554b27538db7fa1bbec52429d8d106effbaeca08"},
+    {file = "backports.zoneinfo-0.2.1-cp36-cp36m-win_amd64.whl", hash = "sha256:8439c030a11780786a2002261569bdf362264f605dfa4d65090b64b05c9f79a7"},
+    {file = "backports.zoneinfo-0.2.1-cp37-cp37m-macosx_10_14_x86_64.whl", hash = "sha256:f04e857b59d9d1ccc39ce2da1021d196e47234873820cbeaad210724b1ee28ac"},
+    {file = "backports.zoneinfo-0.2.1-cp37-cp37m-manylinux1_i686.whl", hash = "sha256:17746bd546106fa389c51dbea67c8b7c8f0d14b5526a579ca6ccf5ed72c526cf"},
+    {file = "backports.zoneinfo-0.2.1-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:5c144945a7752ca544b4b78c8c41544cdfaf9786f25fe5ffb10e838e19a27570"},
+    {file = "backports.zoneinfo-0.2.1-cp37-cp37m-win32.whl", hash = "sha256:e55b384612d93be96506932a786bbcde5a2db7a9e6a4bb4bffe8b733f5b9036b"},
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 description = "An easy safelist-based HTML-sanitizing tool."
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+optional = true
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 ]
 
 [package.dependencies]
@@ -463,99 +504,114 @@ pycparser = "*"
 
 [[package]]
 name = "charset-normalizer"
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 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 category = "main"
 optional = false
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 [package.dependencies]
@@ -622,131 +678,136 @@ test = ["flake8 (==3.7.8)", "hypothesis (==3.55.3)"]
 
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 [[package]]
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+[package.extras]
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+tests = ["pytest", "pytest-cov", "pytest-xdist"]
+
 [[package]]
 name = "dask"
 version = "2022.12.1"
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 description = "XML bomb protection for Python stdlib modules"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
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 [[package]]
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+    {file = "fonttools-4.43.0-cp38-cp38-win_amd64.whl", hash = "sha256:030355fbb0cea59cf75d076d04d3852900583d1258574ff2d7d719abf4513836"},
+    {file = "fonttools-4.43.0-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:52e77f23a9c059f8be01a07300ba4c4d23dc271d33eed502aea5a01ab5d2f4c1"},
+    {file = "fonttools-4.43.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:6a530fa28c155538d32214eafa0964989098a662bd63e91e790e6a7a4e9c02da"},
+    {file = "fonttools-4.43.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:70f021a6b9eb10dfe7a411b78e63a503a06955dd6d2a4e130906d8760474f77c"},
+    {file = "fonttools-4.43.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:812142a0e53cc853964d487e6b40963df62f522b1b571e19d1ff8467d7880ceb"},
+    {file = "fonttools-4.43.0-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:ace51902ab67ef5fe225e8b361039e996db153e467e24a28d35f74849b37b7ce"},
+    {file = "fonttools-4.43.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:8dfd8edfce34ad135bd69de20c77449c06e2c92b38f2a8358d0987737f82b49e"},
+    {file = "fonttools-4.43.0-cp39-cp39-win32.whl", hash = "sha256:e5d53eddaf436fa131042f44a76ea1ead0a17c354ab9de0d80e818f0cb1629f1"},
+    {file = "fonttools-4.43.0-cp39-cp39-win_amd64.whl", hash = "sha256:93c5b6d77baf28f306bc13fa987b0b13edca6a39dc2324eaca299a74ccc6316f"},
+    {file = "fonttools-4.43.0-py3-none-any.whl", hash = "sha256:e4bc589d8da09267c7c4ceaaaa4fc01a7908ac5b43b286ac9279afe76407c384"},
+    {file = "fonttools-4.43.0.tar.gz", hash = "sha256:b62a53a4ca83c32c6b78cac64464f88d02929779373c716f738af6968c8c821e"},
 ]
 
 [package.extras]
@@ -1251,7 +1324,7 @@ name = "fqdn"
 version = "1.5.1"
 description = "Validates fully-qualified domain names against RFC 1123, so that they are acceptable to modern bowsers"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=2.7, !=3.0, !=3.1, !=3.2, !=3.3, !=3.4, <4"
 files = [
     {file = "fqdn-1.5.1-py3-none-any.whl", hash = "sha256:3a179af3761e4df6eb2e026ff9e1a3033d3587bf980a0b1b2e1e5d08d7358014"},
@@ -1260,14 +1333,14 @@ files = [
 
 [[package]]
 name = "fsspec"
-version = "2023.6.0"
+version = "2023.9.2"
 description = "File-system specification"
 category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "fsspec-2023.6.0-py3-none-any.whl", hash = "sha256:1cbad1faef3e391fba6dc005ae9b5bdcbf43005c9167ce78c915549c352c869a"},
-    {file = "fsspec-2023.6.0.tar.gz", hash = "sha256:d0b2f935446169753e7a5c5c55681c54ea91996cc67be93c39a154fb3a2742af"},
+    {file = "fsspec-2023.9.2-py3-none-any.whl", hash = "sha256:603dbc52c75b84da501b9b2ec8c11e1f61c25984c4a0dda1f129ef391fbfc9b4"},
+    {file = "fsspec-2023.9.2.tar.gz", hash = "sha256:80bfb8c70cc27b2178cc62a935ecf242fc6e8c3fb801f9c571fc01b1e715ba7d"},
 ]
 
 [package.extras]
@@ -1296,14 +1369,14 @@ tqdm = ["tqdm"]
 
 [[package]]
 name = "furo"
-version = "2023.7.26"
+version = "2023.9.10"
 description = "A clean customisable Sphinx documentation theme."
 category = "main"
 optional = true
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "furo-2023.7.26-py3-none-any.whl", hash = "sha256:1c7936929ec57c5ddecc7c85f07fa8b2ce536b5c89137764cca508be90e11efd"},
-    {file = "furo-2023.7.26.tar.gz", hash = "sha256:257f63bab97aa85213a1fa24303837a3c3f30be92901ec732fea74290800f59e"},
+    {file = "furo-2023.9.10-py3-none-any.whl", hash = "sha256:513092538537dc5c596691da06e3c370714ec99bc438680edc1debffb73e5bfc"},
+    {file = "furo-2023.9.10.tar.gz", hash = "sha256:5707530a476d2a63b8cad83b4f961f3739a69f4b058bcf38a03a39fa537195b2"},
 ]
 
 [package.dependencies]
@@ -1340,42 +1413,21 @@ smmap = ">=3.0.1,<6"
 
 [[package]]
 name = "gitpython"
-version = "3.1.32"
+version = "3.1.37"
 description = "GitPython is a Python library used to interact with Git repositories"
 category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "GitPython-3.1.32-py3-none-any.whl", hash = "sha256:e3d59b1c2c6ebb9dfa7a184daf3b6dd4914237e7488a1730a6d8f6f5d0b4187f"},
-    {file = "GitPython-3.1.32.tar.gz", hash = "sha256:8d9b8cb1e80b9735e8717c9362079d3ce4c6e5ddeebedd0361b228c3a67a62f6"},
+    {file = "GitPython-3.1.37-py3-none-any.whl", hash = "sha256:5f4c4187de49616d710a77e98ddf17b4782060a1788df441846bddefbb89ab33"},
+    {file = "GitPython-3.1.37.tar.gz", hash = "sha256:f9b9ddc0761c125d5780eab2d64be4873fc6817c2899cbcb34b02344bdc7bc54"},
 ]
 
 [package.dependencies]
 gitdb = ">=4.0.1,<5"
 
-[[package]]
-name = "h5grove"
-version = "1.3.0"
-description = "Core utilities to serve HDF5 file contents"
-category = "main"
-optional = false
-python-versions = ">=3.6"
-files = [
-    {file = "h5grove-1.3.0-py3-none-any.whl", hash = "sha256:340d7b47e9957bd666b92712d62d0841797449774a738ec8f519ed80ba9a13fb"},
-    {file = "h5grove-1.3.0.tar.gz", hash = "sha256:e8f052ff497f0ff42477a24511bbf0d8a1cf0b6e7aea31957bdec7b4baae2c9a"},
-]
-
-[package.dependencies]
-h5py = ">=2.9"
-numpy = "*"
-orjson = "*"
-tifffile = "*"
-
 [package.extras]
-dev = ["black", "bump2version", "check-manifest", "flake8", "h5grove[fastapi]", "h5grove[flask]", "h5grove[tornado]", "httpx (>=0.23)", "invoke", "mypy", "myst-parser", "pytest", "pytest-benchmark", "pytest-cov", "pytest-tornado", "sphinx", "sphinx-argparse", "types-contextvars", "types-dataclasses", "types-orjson", "types-pkg-resources"]
-fastapi = ["fastapi", "uvicorn"]
-flask = ["Flask", "Flask-Compress", "Flask-Cors"]
-tornado = ["tornado"]
+test = ["black", "coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mypy", "pre-commit", "pytest", "pytest-cov", "pytest-sugar"]
 
 [[package]]
 name = "h5py"
@@ -1582,22 +1634,22 @@ testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs
 
 [[package]]
 name = "importlib-resources"
-version = "6.0.1"
+version = "6.1.0"
 description = "Read resources from Python packages"
 category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "importlib_resources-6.0.1-py3-none-any.whl", hash = "sha256:134832a506243891221b88b4ae1213327eea96ceb4e407a00d790bb0626f45cf"},
-    {file = "importlib_resources-6.0.1.tar.gz", hash = "sha256:4359457e42708462b9626a04657c6208ad799ceb41e5c58c57ffa0e6a098a5d4"},
+    {file = "importlib_resources-6.1.0-py3-none-any.whl", hash = "sha256:aa50258bbfa56d4e33fbd8aa3ef48ded10d1735f11532b8df95388cc6bdb7e83"},
+    {file = "importlib_resources-6.1.0.tar.gz", hash = "sha256:9d48dcccc213325e810fd723e7fbb45ccb39f6cf5c31f00cf2b965f5f10f3cb9"},
 ]
 
 [package.dependencies]
 zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""}
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-ruff"]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
+testing = ["pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-ruff", "zipp (>=3.17)"]
 
 [[package]]
 name = "iniconfig"
@@ -1613,14 +1665,14 @@ files = [
 
 [[package]]
 name = "ipykernel"
-version = "6.25.1"
+version = "6.25.2"
 description = "IPython Kernel for Jupyter"
 category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "ipykernel-6.25.1-py3-none-any.whl", hash = "sha256:c8a2430b357073b37c76c21c52184db42f6b4b0e438e1eb7df3c4440d120497c"},
-    {file = "ipykernel-6.25.1.tar.gz", hash = "sha256:050391364c0977e768e354bdb60cbbfbee7cbb943b1af1618382021136ffd42f"},
+    {file = "ipykernel-6.25.2-py3-none-any.whl", hash = "sha256:2e2ee359baba19f10251b99415bb39de1e97d04e1fab385646f24f0596510b77"},
+    {file = "ipykernel-6.25.2.tar.gz", hash = "sha256:f468ddd1f17acb48c8ce67fcfa49ba6d46d4f9ac0438c1f441be7c3d1372230b"},
 ]
 
 [package.dependencies]
@@ -1704,14 +1756,14 @@ test = ["ipython[test]", "pytest", "pytest-asyncio", "pytest-cov", "testpath"]
 
 [[package]]
 name = "ipython"
-version = "8.12.2"
+version = "8.12.3"
 description = "IPython: Productive Interactive Computing"
 category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "ipython-8.12.2-py3-none-any.whl", hash = "sha256:ea8801f15dfe4ffb76dea1b09b847430ffd70d827b41735c64a0638a04103bfc"},
-    {file = "ipython-8.12.2.tar.gz", hash = "sha256:c7b80eb7f5a855a88efc971fda506ff7a91c280b42cdae26643e0f601ea281ea"},
+    {file = "ipython-8.12.3-py3-none-any.whl", hash = "sha256:b0340d46a933d27c657b211a329d0be23793c36595acf9e6ef4164bc01a1804c"},
+    {file = "ipython-8.12.3.tar.gz", hash = "sha256:3910c4b54543c2ad73d06579aa771041b7d5707b033bd488669b4cf544e3b363"},
 ]
 
 [package.dependencies]
@@ -1756,33 +1808,32 @@ files = [
 
 [[package]]
 name = "ipywidgets"
-version = "7.8.0"
-description = "IPython HTML widgets for Jupyter"
+version = "8.1.1"
+description = "Jupyter interactive widgets"
 category = "main"
 optional = false
-python-versions = "*"
+python-versions = ">=3.7"
 files = [
-    {file = "ipywidgets-7.8.0-py2.py3-none-any.whl", hash = "sha256:47e5d57c2f73f6464ea422978106a119ab2721d88e2d4982282ebb54eb59a88e"},
-    {file = "ipywidgets-7.8.0.tar.gz", hash = "sha256:6412eb61d1ccde049f731084266b3c946117b3e8c2769923fc20ea35633ef77c"},
+    {file = "ipywidgets-8.1.1-py3-none-any.whl", hash = "sha256:2b88d728656aea3bbfd05d32c747cfd0078f9d7e159cf982433b58ad717eed7f"},
+    {file = "ipywidgets-8.1.1.tar.gz", hash = "sha256:40211efb556adec6fa450ccc2a77d59ca44a060f4f9f136833df59c9f538e6e8"},
 ]
 
 [package.dependencies]
 comm = ">=0.1.3"
-ipython = {version = ">=4.0.0", markers = "python_version >= \"3.3\""}
-ipython-genutils = ">=0.2.0,<0.3.0"
-jupyterlab-widgets = {version = ">=1.0.0,<3", markers = "python_version >= \"3.6\""}
+ipython = ">=6.1.0"
+jupyterlab-widgets = ">=3.0.9,<3.1.0"
 traitlets = ">=4.3.1"
-widgetsnbextension = ">=3.6.5,<3.7.0"
+widgetsnbextension = ">=4.0.9,<4.1.0"
 
 [package.extras]
-test = ["ipykernel", "mock", "pytest (>=3.6.0)", "pytest-cov"]
+test = ["ipykernel", "jsonschema", "pytest (>=3.6.0)", "pytest-cov", "pytz"]
 
 [[package]]
 name = "isoduration"
 version = "20.11.0"
 description = "Operations with ISO 8601 durations"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.7"
 files = [
     {file = "isoduration-20.11.0-py3-none-any.whl", hash = "sha256:b2904c2a4228c3d44f409c8ae8e2370eb21a26f7ac2ec5446df141dde3452042"},
@@ -1794,14 +1845,14 @@ arrow = ">=0.15.0"
 
 [[package]]
 name = "jedi"
-version = "0.19.0"
+version = "0.19.1"
 description = "An autocompletion tool for Python that can be used for text editors."
 category = "main"
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "jedi-0.19.0-py2.py3-none-any.whl", hash = "sha256:cb8ce23fbccff0025e9386b5cf85e892f94c9b822378f8da49970471335ac64e"},
-    {file = "jedi-0.19.0.tar.gz", hash = "sha256:bcf9894f1753969cbac8022a8c2eaee06bfa3724e4192470aaffe7eb6272b0c4"},
+    {file = "jedi-0.19.1-py2.py3-none-any.whl", hash = "sha256:e983c654fe5c02867aef4cdfce5a2fbb4a50adc0af145f70504238f18ef5e7e0"},
+    {file = "jedi-0.19.1.tar.gz", hash = "sha256:cf0496f3651bc65d7174ac1b7d043eff454892c708a87d1b683e57b569927ffd"},
 ]
 
 [package.dependencies]
@@ -1810,7 +1861,7 @@ parso = ">=0.8.3,<0.9.0"
 [package.extras]
 docs = ["Jinja2 (==2.11.3)", "MarkupSafe (==1.1.1)", "Pygments (==2.8.1)", "alabaster (==0.7.12)", "babel (==2.9.1)", "chardet (==4.0.0)", "commonmark (==0.8.1)", "docutils (==0.17.1)", "future (==0.18.2)", "idna (==2.10)", "imagesize (==1.2.0)", "mock (==1.0.1)", "packaging (==20.9)", "pyparsing (==2.4.7)", "pytz (==2021.1)", "readthedocs-sphinx-ext (==2.1.4)", "recommonmark (==0.5.0)", "requests (==2.25.1)", "six (==1.15.0)", "snowballstemmer (==2.1.0)", "sphinx (==1.8.5)", "sphinx-rtd-theme (==0.4.3)", "sphinxcontrib-serializinghtml (==1.1.4)", "sphinxcontrib-websupport (==1.2.4)", "urllib3 (==1.26.4)"]
 qa = ["flake8 (==5.0.4)", "mypy (==0.971)", "types-setuptools (==67.2.0.1)"]
-testing = ["Django (<3.1)", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]
+testing = ["Django", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]
 
 [[package]]
 name = "jinja2"
@@ -1847,7 +1898,7 @@ name = "json5"
 version = "0.9.14"
 description = "A Python implementation of the JSON5 data format."
 category = "main"
-optional = false
+optional = true
 python-versions = "*"
 files = [
     {file = "json5-0.9.14-py2.py3-none-any.whl", hash = "sha256:740c7f1b9e584a468dbb2939d8d458db3427f2c93ae2139d05f47e453eae964f"},
@@ -1862,7 +1913,7 @@ name = "jsonpointer"
 version = "2.4"
 description = "Identify specific nodes in a JSON document (RFC 6901)"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*"
 files = [
     {file = "jsonpointer-2.4-py2.py3-none-any.whl", hash = "sha256:15d51bba20eea3165644553647711d150376234112651b4f1811022aecad7d7a"},
@@ -1871,14 +1922,14 @@ files = [
 
 [[package]]
 name = "jsonschema"
-version = "4.19.0"
+version = "4.19.1"
 description = "An implementation of JSON Schema validation for Python"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.8"
 files = [
-    {file = "jsonschema-4.19.0-py3-none-any.whl", hash = "sha256:043dc26a3845ff09d20e4420d6012a9c91c9aa8999fa184e7efcfeccb41e32cb"},
-    {file = "jsonschema-4.19.0.tar.gz", hash = "sha256:6e1e7569ac13be8139b2dd2c21a55d350066ee3f80df06c608b398cdc6f30e8f"},
+    {file = "jsonschema-4.19.1-py3-none-any.whl", hash = "sha256:cd5f1f9ed9444e554b38ba003af06c0a8c2868131e56bfbef0550fb450c0330e"},
+    {file = "jsonschema-4.19.1.tar.gz", hash = "sha256:ec84cc37cfa703ef7cd4928db24f9cb31428a5d0fa77747b8b51a847458e0bbf"},
 ]
 
 [package.dependencies]
@@ -1906,7 +1957,7 @@ name = "jsonschema-specifications"
 version = "2023.7.1"
 description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.8"
 files = [
     {file = "jsonschema_specifications-2023.7.1-py3-none-any.whl", hash = "sha256:05adf340b659828a004220a9613be00fa3f223f2b82002e273dee62fd50524b1"},
@@ -1940,14 +1991,14 @@ qtconsole = "*"
 
 [[package]]
 name = "jupyter-client"
-version = "8.3.0"
+version = "8.3.1"
 description = "Jupyter protocol implementation and client libraries"
 category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_client-8.3.0-py3-none-any.whl", hash = "sha256:7441af0c0672edc5d28035e92ba5e32fadcfa8a4e608a434c228836a89df6158"},
-    {file = "jupyter_client-8.3.0.tar.gz", hash = "sha256:3af69921fe99617be1670399a0b857ad67275eefcfa291e2c81a160b7b650f5f"},
+    {file = "jupyter_client-8.3.1-py3-none-any.whl", hash = "sha256:5eb9f55eb0650e81de6b7e34308d8b92d04fe4ec41cd8193a913979e33d8e1a5"},
+    {file = "jupyter_client-8.3.1.tar.gz", hash = "sha256:60294b2d5b869356c893f57b1a877ea6510d60d45cf4b38057f1672d85699ac9"},
 ]
 
 [package.dependencies]
@@ -1989,14 +2040,14 @@ test = ["flaky", "pexpect", "pytest"]
 
 [[package]]
 name = "jupyter-core"
-version = "5.3.1"
+version = "5.3.2"
 description = "Jupyter core package. A base package on which Jupyter projects rely."
 category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_core-5.3.1-py3-none-any.whl", hash = "sha256:ae9036db959a71ec1cac33081eeb040a79e681f08ab68b0883e9a676c7a90dce"},
-    {file = "jupyter_core-5.3.1.tar.gz", hash = "sha256:5ba5c7938a7f97a6b0481463f7ff0dbac7c15ba48cf46fa4035ca6e838aa1aba"},
+    {file = "jupyter_core-5.3.2-py3-none-any.whl", hash = "sha256:a4af53c3fa3f6330cebb0d9f658e148725d15652811d1c32dc0f63bb96f2e6d6"},
+    {file = "jupyter_core-5.3.2.tar.gz", hash = "sha256:0c28db6cbe2c37b5b398e1a1a5b22f84fd64cd10afc1f6c05b02fb09481ba45f"},
 ]
 
 [package.dependencies]
@@ -2013,7 +2064,7 @@ name = "jupyter-events"
 version = "0.7.0"
 description = "Jupyter Event System library"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.8"
 files = [
     {file = "jupyter_events-0.7.0-py3-none-any.whl", hash = "sha256:4753da434c13a37c3f3c89b500afa0c0a6241633441421f6adafe2fb2e2b924e"},
@@ -2034,16 +2085,32 @@ cli = ["click", "rich"]
 docs = ["jupyterlite-sphinx", "myst-parser", "pydata-sphinx-theme", "sphinxcontrib-spelling"]
 test = ["click", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.19.0)", "pytest-console-scripts", "rich"]
 
+[[package]]
+name = "jupyter-lsp"
+version = "2.2.0"
+description = "Multi-Language Server WebSocket proxy for Jupyter Notebook/Lab server"
+category = "main"
+optional = true
+python-versions = ">=3.8"
+files = [
+    {file = "jupyter-lsp-2.2.0.tar.gz", hash = "sha256:8ebbcb533adb41e5d635eb8fe82956b0aafbf0fd443b6c4bfa906edeeb8635a1"},
+    {file = "jupyter_lsp-2.2.0-py3-none-any.whl", hash = "sha256:9e06b8b4f7dd50300b70dd1a78c0c3b0c3d8fa68e0f2d8a5d1fbab62072aca3f"},
+]
+
+[package.dependencies]
+importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""}
+jupyter-server = ">=1.1.2"
+
 [[package]]
 name = "jupyter-server"
-version = "2.7.0"
+version = "2.7.3"
 description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications."
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_server-2.7.0-py3-none-any.whl", hash = "sha256:6a77912aff643e53fa14bdb2634884b52b784a4be77ce8e93f7283faed0f0849"},
-    {file = "jupyter_server-2.7.0.tar.gz", hash = "sha256:36da0a266d31a41ac335a366c88933c17dfa5bb817a48f5c02c16d303bc9477f"},
+    {file = "jupyter_server-2.7.3-py3-none-any.whl", hash = "sha256:8e4b90380b59d7a1e31086c4692231f2a2ea4cb269f5516e60aba72ce8317fc9"},
+    {file = "jupyter_server-2.7.3.tar.gz", hash = "sha256:d4916c8581c4ebbc534cebdaa8eca2478d9f3bfdd88eae29fcab0120eac57649"},
 ]
 
 [package.dependencies]
@@ -2061,7 +2128,7 @@ packaging = "*"
 prometheus-client = "*"
 pywinpty = {version = "*", markers = "os_name == \"nt\""}
 pyzmq = ">=24"
-send2trash = "*"
+send2trash = ">=1.8.2"
 terminado = ">=0.8.3"
 tornado = ">=6.2.0"
 traitlets = ">=5.6.0"
@@ -2076,7 +2143,7 @@ name = "jupyter-server-terminals"
 version = "0.4.4"
 description = "A Jupyter Server Extension Providing Terminals."
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.8"
 files = [
     {file = "jupyter_server_terminals-0.4.4-py3-none-any.whl", hash = "sha256:75779164661cec02a8758a5311e18bb8eb70c4e86c6b699403100f1585a12a36"},
@@ -2093,57 +2160,44 @@ test = ["coverage", "jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-cov",
 
 [[package]]
 name = "jupyterlab"
-version = "3.5.3"
+version = "4.0.6"
 description = "JupyterLab computational environment"
 category = "main"
-optional = false
-python-versions = ">=3.7"
+optional = true
+python-versions = ">=3.8"
 files = [
-    {file = "jupyterlab-3.5.3-py3-none-any.whl", hash = "sha256:8e1a4414b681dafd3f19bd45cb0c79cb713bc78ef4e8440b95d86881c23a9fe5"},
-    {file = "jupyterlab-3.5.3.tar.gz", hash = "sha256:51e889448ae194eeef8e50f63f5c4f487f728f477befe436e9749672f7511dbe"},
+    {file = "jupyterlab-4.0.6-py3-none-any.whl", hash = "sha256:7d9dacad1e3f30fe4d6d4efc97fda25fbb5012012b8f27cc03a2283abcdee708"},
+    {file = "jupyterlab-4.0.6.tar.gz", hash = "sha256:6c43ae5a6a1fd2fdfafcb3454004958bde6da76331abb44cffc6f9e436b19ba1"},
 ]
 
 [package.dependencies]
-ipython = "*"
-jinja2 = ">=2.1"
+async-lru = ">=1.0.0"
+importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""}
+importlib-resources = {version = ">=1.4", markers = "python_version < \"3.9\""}
+ipykernel = "*"
+jinja2 = ">=3.0.3"
 jupyter-core = "*"
-jupyter-server = ">=1.16.0,<3"
-jupyterlab-server = ">=2.10,<3.0"
-nbclassic = "*"
-notebook = "<7"
+jupyter-lsp = ">=2.0.0"
+jupyter-server = ">=2.4.0,<3"
+jupyterlab-server = ">=2.19.0,<3"
+notebook-shim = ">=0.2"
 packaging = "*"
-tomli = "*"
-tornado = ">=6.1.0"
-
-[package.extras]
-test = ["check-manifest", "coverage", "jupyterlab-server[test]", "pre-commit", "pytest (>=6.0)", "pytest-check-links (>=0.5)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter (>=0.6.0)", "pytest-tornasync", "requests", "requests-cache", "virtualenv"]
-
-[[package]]
-name = "jupyterlab-h5web"
-version = "10.0.0"
-description = "A JupyterLab extension to explore and visualize HDF5 file contents."
-category = "main"
-optional = false
-python-versions = ">=3.8"
-files = [
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-]
-
-[package.dependencies]
-h5grove = "1.3.0"
-h5py = ">=3.5"
-jupyter-server = ">=1.6,<3"
+tomli = {version = "*", markers = "python_version < \"3.11\""}
+tornado = ">=6.2.0"
+traitlets = "*"
 
 [package.extras]
-full = ["hdf5plugin"]
+dev = ["black[jupyter] (==23.7.0)", "build", "bump2version", "coverage", "hatch", "pre-commit", "pytest-cov", "ruff (==0.0.286)"]
+docs = ["jsx-lexer", "myst-parser", "pydata-sphinx-theme (>=0.13.0)", "pytest", "pytest-check-links", "pytest-tornasync", "sphinx (>=1.8,<7.2.0)", "sphinx-copybutton"]
+docs-screenshots = ["altair (==5.0.1)", "ipython (==8.14.0)", "ipywidgets (==8.0.6)", "jupyterlab-geojson (==3.4.0)", "jupyterlab-language-pack-zh-cn (==4.0.post0)", "matplotlib (==3.7.1)", "nbconvert (>=7.0.0)", "pandas (==2.0.2)", "scipy (==1.10.1)", "vega-datasets (==0.9.0)"]
+test = ["coverage", "pytest (>=7.0)", "pytest-check-links (>=0.7)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter (>=0.5.3)", "pytest-timeout", "pytest-tornasync", "requests", "requests-cache", "virtualenv"]
 
 [[package]]
 name = "jupyterlab-pygments"
 version = "0.2.2"
 description = "Pygments theme using JupyterLab CSS variables"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.7"
 files = [
     {file = "jupyterlab_pygments-0.2.2-py2.py3-none-any.whl", hash = "sha256:2405800db07c9f770863bcf8049a529c3dd4d3e28536638bd7c1c01d2748309f"},
@@ -2152,14 +2206,14 @@ files = [
 
 [[package]]
 name = "jupyterlab-server"
-version = "2.24.0"
+version = "2.25.0"
 description = "A set of server components for JupyterLab and JupyterLab like applications."
 category = "main"
-optional = false
-python-versions = ">=3.7"
+optional = true
+python-versions = ">=3.8"
 files = [
-    {file = "jupyterlab_server-2.24.0-py3-none-any.whl", hash = "sha256:5f077e142bb8dc9b843d960f940c513581bceca3793a0d80f9c67d9522c4e876"},
-    {file = "jupyterlab_server-2.24.0.tar.gz", hash = "sha256:4e6f99e0a5579bbbc32e449c4dbb039561d4f1a7827d5733273ed56738f21f07"},
+    {file = "jupyterlab_server-2.25.0-py3-none-any.whl", hash = "sha256:c9f67a98b295c5dee87f41551b0558374e45d449f3edca153dd722140630dcb2"},
+    {file = "jupyterlab_server-2.25.0.tar.gz", hash = "sha256:77c2f1f282d610f95e496e20d5bf1d2a7706826dfb7b18f3378ae2870d272fb7"},
 ]
 
 [package.dependencies]
@@ -2167,26 +2221,26 @@ babel = ">=2.10"
 importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""}
 jinja2 = ">=3.0.3"
 json5 = ">=0.9.0"
-jsonschema = ">=4.17.3"
+jsonschema = ">=4.18.0"
 jupyter-server = ">=1.21,<3"
 packaging = ">=21.3"
-requests = ">=2.28"
+requests = ">=2.31"
 
 [package.extras]
 docs = ["autodoc-traits", "jinja2 (<3.2.0)", "mistune (<4)", "myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-copybutton", "sphinxcontrib-openapi (>0.8)"]
-openapi = ["openapi-core (>=0.16.1,<0.17.0)", "ruamel-yaml"]
-test = ["hatch", "ipykernel", "jupyterlab-server[openapi]", "openapi-spec-validator (>=0.5.1,<0.7.0)", "pytest (>=7.0)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter[server] (>=0.6.2)", "pytest-timeout", "requests-mock", "sphinxcontrib-spelling", "strict-rfc3339", "werkzeug"]
+openapi = ["openapi-core (>=0.18.0,<0.19.0)", "ruamel-yaml"]
+test = ["hatch", "ipykernel", "openapi-core (>=0.18.0,<0.19.0)", "openapi-spec-validator (>=0.6.0,<0.7.0)", "pytest (>=7.0)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter[server] (>=0.6.2)", "pytest-timeout", "requests-mock", "ruamel-yaml", "sphinxcontrib-spelling", "strict-rfc3339", "werkzeug"]
 
 [[package]]
 name = "jupyterlab-widgets"
-version = "1.1.5"
-description = "A JupyterLab extension."
+version = "3.0.9"
+description = "Jupyter interactive widgets for JupyterLab"
 category = "main"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 files = [
-    {file = "jupyterlab_widgets-1.1.5-py3-none-any.whl", hash = "sha256:7bc9a5642f6741945e873183695c28b02665a7f075aa36f7cca8b52ab984e581"},
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+    {file = "jupyterlab_widgets-3.0.9-py3-none-any.whl", hash = "sha256:3cf5bdf5b897bf3bccf1c11873aa4afd776d7430200f765e0686bd352487b58d"},
+    {file = "jupyterlab_widgets-3.0.9.tar.gz", hash = "sha256:6005a4e974c7beee84060fdfba341a3218495046de8ae3ec64888e5fe19fdb4c"},
 ]
 
 [[package]]
@@ -2228,80 +2282,116 @@ viz = ["matplotlib (>=3.5)", "pyvista"]
 
 [[package]]
 name = "kiwisolver"
-version = "1.4.4"
+version = "1.4.5"
 description = "A fast implementation of the Cassowary constraint solver"
 category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -2554,11 +2650,12 @@ cycler = ">=0.10"
 fonttools = ">=4.22.0"
 importlib-resources = {version = ">=3.2.0", markers = "python_version < \"3.10\""}
 kiwisolver = ">=1.0.1"
-numpy = ">=1.20"
+numpy = ">=1.20,<2"
 packaging = ">=20.0"
 pillow = ">=6.2.0"
-pyparsing = ">=2.3.1,<3.1"
+pyparsing = ">=2.3.1"
 python-dateutil = ">=2.7"
+setuptools_scm = ">=7"
 
 [[package]]
 name = "matplotlib-inline"
@@ -2604,14 +2701,14 @@ files = [
 
 [[package]]
 name = "mistune"
-version = "3.0.1"
+version = "3.0.2"
 description = "A sane and fast Markdown parser with useful plugins and renderers"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.7"
 files = [
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 ]
 
 [[package]]
@@ -2648,48 +2745,12 @@ files = [
 fast = ["fastnumbers (>=2.0.0)"]
 icu = ["PyICU (>=1.0.0)"]
 
-[[package]]
-name = "nbclassic"
-version = "1.0.0"
-description = "Jupyter Notebook as a Jupyter Server extension."
-category = "main"
-optional = false
-python-versions = ">=3.7"
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-
-[package.dependencies]
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-Send2Trash = ">=1.8.0"
-terminado = ">=0.8.3"
-tornado = ">=6.1"
-traitlets = ">=4.2.1"
-
-[package.extras]
-docs = ["myst-parser", "nbsphinx", "sphinx", "sphinx-rtd-theme", "sphinxcontrib-github-alt"]
-json-logging = ["json-logging"]
-test = ["coverage", "nbval", "pytest", "pytest-cov", "pytest-jupyter", "pytest-playwright", "pytest-tornasync", "requests", "requests-unixsocket", "testpath"]
-
 [[package]]
 name = "nbclient"
 version = "0.8.0"
 description = "A client library for executing notebooks. Formerly nbconvert's ExecutePreprocessor."
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.8.0"
 files = [
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@@ -2709,14 +2770,14 @@ test = ["flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "nbconvert (>=
 
 [[package]]
 name = "nbconvert"
-version = "7.7.3"
+version = "7.9.2"
 description = "Converting Jupyter Notebooks"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -2743,7 +2804,7 @@ docs = ["ipykernel", "ipython", "myst-parser", "nbsphinx (>=0.2.12)", "pydata-sp
 qtpdf = ["nbconvert[qtpng]"]
 qtpng = ["pyqtwebengine (>=5.15)"]
 serve = ["tornado (>=6.1)"]
-test = ["flaky", "ipykernel", "ipywidgets (>=7)", "pre-commit", "pytest", "pytest-dependency"]
+test = ["flaky", "ipykernel", "ipywidgets (>=7)", "pytest", "pytest-dependency"]
 webpdf = ["playwright"]
 
 [[package]]
@@ -2751,7 +2812,7 @@ name = "nbformat"
 version = "5.9.2"
 description = "The Jupyter Notebook format"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.8"
 files = [
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@@ -2770,14 +2831,14 @@ test = ["pep440", "pre-commit", "pytest", "testpath"]
 
 [[package]]
 name = "nest-asyncio"
-version = "1.5.7"
+version = "1.5.8"
 description = "Patch asyncio to allow nested event loops"
 category = "main"
 optional = false
 python-versions = ">=3.5"
 files = [
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 ]
 
 [[package]]
@@ -2800,46 +2861,124 @@ extra = ["lxml (>=4.6)", "pydot (>=1.4.2)", "pygraphviz (>=1.10)", "sympy (>=1.1
 test = ["codecov (>=2.1)", "pytest (>=7.2)", "pytest-cov (>=4.0)"]
 
 [[package]]
-name = "notebook"
-version = "6.5.4"
-description = "A web-based notebook environment for interactive computing"
+name = "niondata"
+version = "0.15.3"
+description = "A data processing library for Nion Swift."
 category = "main"
 optional = false
-python-versions = ">=3.7"
+python-versions = "~=3.8"
 files = [
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 ]
 
 [package.dependencies]
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-prometheus-client = "*"
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-Send2Trash = ">=1.8.0"
-terminado = ">=0.8.3"
-tornado = ">=6.1"
-traitlets = ">=4.2.1"
+nionutils = ">=0.4.5,<0.5.0"
+numpy = ">=1.21,<2.0"
+scipy = "*"
+
+[[package]]
+name = "nionswift"
+version = "0.16.8"
+description = "Nion Swift: Scientific Image Processing."
+category = "main"
+optional = false
+python-versions = "<3.12,>=3.8"
+files = [
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+pytz = "*"
+scipy = "*"
+tzlocal = "*"
+
+[[package]]
+name = "nionswift-io"
+version = "0.15.1"
+description = "NionSwift IO handlers."
+category = "main"
+optional = false
+python-versions = "~=3.8"
+files = [
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+nionutils = ">=0.4.0,<0.5.0"
+numpy = ">=1.21,<2.0"
+
+[[package]]
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+version = "0.6.10"
+description = "Nion UI framework."
+category = "main"
+optional = false
+python-versions = "~=3.8"
+files = [
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+
+[[package]]
+name = "nionutils"
+version = "0.4.6"
+description = "Nion utility classes."
+category = "main"
+optional = false
+python-versions = "~=3.8"
+files = [
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+]
+
+[[package]]
+name = "notebook"
+version = "7.0.4"
+description = "Jupyter Notebook - A web-based notebook environment for interactive computing"
+category = "main"
+optional = true
+python-versions = ">=3.8"
+files = [
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+
+[package.dependencies]
+jupyter-server = ">=2.4.0,<3"
+jupyterlab = ">=4.0.2,<5"
+jupyterlab-server = ">=2.22.1,<3"
+notebook-shim = ">=0.2,<0.3"
+tornado = ">=6.2.0"
 
 [package.extras]
-docs = ["myst-parser", "nbsphinx", "sphinx", "sphinx-rtd-theme", "sphinxcontrib-github-alt"]
-json-logging = ["json-logging"]
-test = ["coverage", "nbval", "pytest", "pytest-cov", "requests", "requests-unixsocket", "selenium (==4.1.5)", "testpath"]
+dev = ["hatch", "pre-commit"]
+docs = ["myst-parser", "nbsphinx", "pydata-sphinx-theme", "sphinx (>=1.3.6)", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"]
+test = ["importlib-resources (>=5.0)", "ipykernel", "jupyter-server[test] (>=2.4.0,<3)", "jupyterlab-server[test] (>=2.22.1,<3)", "nbval", "pytest (>=7.0)", "pytest-console-scripts", "pytest-timeout", "pytest-tornasync", "requests"]
 
 [[package]]
 name = "notebook-shim"
 version = "0.2.3"
 description = "A shim layer for notebook traits and config"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.7"
 files = [
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@@ -2930,42 +3069,42 @@ zfpy = ["zfpy (>=1.0.0)"]
 
 [[package]]
 name = "numexpr"
-version = "2.8.5"
+version = "2.8.6"
 description = "Fast numerical expression evaluator for NumPy"
 category = "main"
 optional = false
 python-versions = ">=3.7"
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 ]
 
 [package.dependencies]
@@ -3129,78 +3268,12 @@ dev = ["black[jupyter]", "coverage (>=5.0)", "ipykernel", "isort (>=5.10)", "man
 doc = ["ipykernel", "memory-profiler", "nbsphinx (>=0.7)", "numpydoc", "pydata-sphinx-theme (>=0.13.1)", "scikit-image", "scikit-learn", "sphinx (>=3.0.2)", "sphinx-codeautolink[ipython]", "sphinx-copybutton (>=0.2.5)", "sphinx-design", "sphinx-gallery (<0.11)", "sphinx-last-updated-by-git", "sphinxcontrib-bibtex (>=1.0)"]
 tests = ["coverage (>=5.0)", "numpydoc", "pytest (>=5.4)", "pytest-cov (>=2.8.1)", "pytest-xdist"]
 
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 description = "A decorator to automatically detect mismatch when overriding a method."
 category = "main"
-optional = false
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 python-versions = ">=3.6"
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+version = "23.2"
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 python-versions = ">=3.7"
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 description = "Resolve a name to an object."
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.6"
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 category = "main"
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 python-versions = ">=3.7"
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 description = "A simple Python library for easily displaying tabular data in a visually appealing ASCII table format"
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 python-versions = ">=3.8"
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@@ -3574,7 +3645,7 @@ name = "prometheus-client"
 version = "0.17.1"
 description = "Python client for the Prometheus monitoring system."
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.6"
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 name = "pyfai"
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+version = "2023.9.0"
 description = "Python implementation of fast azimuthal integration"
 category = "main"
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 python-versions = ">=3.7"
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 ]
 
 [package.dependencies]
@@ -3817,14 +3888,14 @@ plugins = ["importlib-metadata"]
 
 [[package]]
 name = "pynxtools"
-version = "0.0.2"
+version = "0.0.5"
 description = "Extend NeXus for materials science experiment and serve as a NOMAD parser implementation for NeXus."
 category = "main"
 optional = false
 python-versions = "<3.11,>=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -3834,11 +3905,10 @@ flatdict = ">=4.0.1"
 gitpython = ">=3.1.24"
 h5py = ">=3.6.0"
 hyperspy = ">=1.7.4"
-ifes-apt-tc-data-modeling = ">=0.0.8"
-jupyterlab = ">=3.5.2,<3.6.0"
-jupyterlab-h5web = ">=7.0.0"
+ifes-apt-tc-data-modeling = ">=0.0.9"
 kikuchipy = ">=0.8.2"
 lark = ">=1.1.5"
+nionswift = "0.16.8"
 numpy = ">=1.21.2"
 pandas = ">=1.3.2"
 pytz = ">=2021.1"
@@ -3847,6 +3917,7 @@ PyYAML = ">=6.0"
 requests = "*"
 requests-cache = "*"
 scipy = ">=1.7.1"
+tzlocal = "<=4.3"
 xarray = ">=0.20.2"
 zipfile37 = "0.1.3"
 
@@ -3855,14 +3926,14 @@ dev = ["mypy", "pip-tools", "pycodestyle", "pylint", "pytest", "pytest-cov", "py
 
 [[package]]
 name = "pyparsing"
-version = "3.0.9"
+version = "3.1.1"
 description = "pyparsing module - Classes and methods to define and execute parsing grammars"
 category = "main"
 optional = false
 python-versions = ">=3.6.8"
 files = [
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 ]
 
 [package.extras]
@@ -3870,14 +3941,14 @@ diagrams = ["jinja2", "railroad-diagrams"]
 
 [[package]]
 name = "pytest"
-version = "7.4.0"
+version = "7.4.2"
 description = "pytest: simple powerful testing with Python"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -3984,7 +4055,7 @@ name = "python-json-logger"
 version = "2.0.7"
 description = "A python library adding a json log formatter"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.6"
 files = [
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@@ -3993,16 +4064,32 @@ files = [
 
 [[package]]
 name = "pytz"
-version = "2023.3"
+version = "2023.3.post1"
 description = "World timezone definitions, modern and historical"
 category = "main"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
+[[package]]
+name = "pytz-deprecation-shim"
+version = "0.1.0.post0"
+description = "Shims to make deprecation of pytz easier"
+category = "main"
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
+files = [
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+    {file = "pytz_deprecation_shim-0.1.0.post0.tar.gz", hash = "sha256:af097bae1b616dde5c5744441e2ddc69e74dfdcb0c263129610d85b87445a59d"},
+]
+
+[package.dependencies]
+"backports.zoneinfo" = {version = "*", markers = "python_version >= \"3.6\" and python_version < \"3.9\""}
+tzdata = {version = "*", markers = "python_version >= \"3.6\""}
+
 [[package]]
 name = "pywavelets"
 version = "1.4.1"
@@ -4070,7 +4157,7 @@ name = "pywinpty"
 version = "2.0.11"
 description = "Pseudo terminal support for Windows from Python."
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.8"
 files = [
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@@ -4273,14 +4360,14 @@ cffi = {version = "*", markers = "implementation_name == \"pypy\""}
 
 [[package]]
 name = "qtconsole"
-version = "5.4.3"
+version = "5.4.4"
 description = "Jupyter Qt console"
 category = "main"
 optional = true
 python-versions = ">= 3.7"
 files = [
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+    {file = "qtconsole-5.4.4-py3-none-any.whl", hash = "sha256:a3b69b868e041c2c698bdc75b0602f42e130ffb256d6efa48f9aa756c97672aa"},
+    {file = "qtconsole-5.4.4.tar.gz", hash = "sha256:b7ffb53d74f23cee29f4cdb55dd6fabc8ec312d94f3c46ba38e1dde458693dfb"},
 ]
 
 [package.dependencies]
@@ -4291,7 +4378,7 @@ jupyter-core = "*"
 packaging = "*"
 pygments = "*"
 pyzmq = ">=17.1"
-qtpy = ">=2.0.1"
+qtpy = ">=2.4.0"
 traitlets = "<5.2.1 || >5.2.1,<5.2.2 || >5.2.2"
 
 [package.extras]
@@ -4300,14 +4387,14 @@ test = ["flaky", "pytest", "pytest-qt"]
 
 [[package]]
 name = "qtpy"
-version = "2.3.1"
+version = "2.4.0"
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@@ -4318,14 +4405,14 @@ test = ["pytest (>=6,!=7.0.0,!=7.0.1)", "pytest-cov (>=3.0.0)", "pytest-qt"]
 
 [[package]]
 name = "radioactivedecay"
-version = "0.4.20"
+version = "0.4.21"
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@@ -4359,7 +4446,7 @@ name = "referencing"
 version = "0.30.2"
 description = "JSON Referencing + Python"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.8"
 files = [
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@@ -4428,7 +4515,7 @@ name = "rfc3339-validator"
 version = "0.1.4"
 description = "A pure python RFC3339 validator"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
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 version = "0.1.1"
 description = "Pure python rfc3986 validator"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
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 [package.dependencies]
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-numpy = ">=1.17.3"
+numpy = ">=1.17.3,<2.0"
 scipy = ">=1.5.0"
 threadpoolctl = ">=2.0.0"
 
@@ -4715,7 +4804,7 @@ name = "send2trash"
 version = "1.8.2"
 description = "Send file to trash natively under Mac OS X, Windows and Linux"
 category = "main"
-optional = false
+optional = true
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7"
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@@ -4729,20 +4818,43 @@ win32 = ["pywin32"]
 
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 category = "main"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
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+
+[package.extras]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
+testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pip (>=19.1)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-ruff", "pytest-timeout", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
+testing-integration = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "packaging (>=23.1)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
+
+[[package]]
+name = "setuptools-scm"
+version = "8.0.4"
+description = "the blessed package to manage your versions by scm tags"
+category = "main"
+optional = false
+python-versions = ">=3.8"
 files = [
-    {file = "setuptools-68.0.0-py3-none-any.whl", hash = "sha256:11e52c67415a381d10d6b462ced9cfb97066179f0e871399e006c4ab101fc85f"},
-    {file = "setuptools-68.0.0.tar.gz", hash = "sha256:baf1fdb41c6da4cd2eae722e135500da913332ab3f2f5c7d33af9b492acb5235"},
+    {file = "setuptools-scm-8.0.4.tar.gz", hash = "sha256:b5f43ff6800669595193fd09891564ee9d1d7dcb196cab4b2506d53a2e1c95c7"},
+    {file = "setuptools_scm-8.0.4-py3-none-any.whl", hash = "sha256:b47844cd2a84b83b3187a5782c71128c28b4c94cad8bfb871da2784a5cb54c4f"},
 ]
 
+[package.dependencies]
+packaging = ">=20"
+setuptools = "*"
+tomli = {version = ">=1", markers = "python_version < \"3.11\""}
+typing-extensions = "*"
+
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (==0.8.3)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
-testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pip (>=19.1)", "pip-run (>=8.8)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-ruff", "pytest-timeout", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
-testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
+docs = ["entangled-cli[rich]", "mkdocs", "mkdocs-entangled-plugin", "mkdocs-material", "mkdocstrings[python]", "pygments"]
+rich = ["rich"]
+test = ["build", "pytest", "rich", "wheel"]
 
 [[package]]
 name = "silx"
@@ -4805,14 +4917,14 @@ files = [
 
 [[package]]
 name = "smmap"
-version = "5.0.0"
+version = "5.0.1"
 description = "A pure Python implementation of a sliding window memory map manager"
 category = "main"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 files = [
-    {file = "smmap-5.0.0-py3-none-any.whl", hash = "sha256:2aba19d6a040e78d8b09de5c57e96207b09ed71d8e55ce0959eeee6c8e190d94"},
-    {file = "smmap-5.0.0.tar.gz", hash = "sha256:c840e62059cd3be204b0c9c9f74be2c09d5648eddd4580d9314c3ecde0b30936"},
+    {file = "smmap-5.0.1-py3-none-any.whl", hash = "sha256:e6d8668fa5f93e706934a62d7b4db19c8d9eb8cf2adbb75ef1b675aa332b69da"},
+    {file = "smmap-5.0.1.tar.gz", hash = "sha256:dceeb6c0028fdb6734471eb07c0cd2aae706ccaecab45965ee83f11c8d3b1f62"},
 ]
 
 [[package]]
@@ -4820,7 +4932,7 @@ name = "sniffio"
 version = "1.3.0"
 description = "Sniff out which async library your code is running under"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.7"
 files = [
     {file = "sniffio-1.3.0-py3-none-any.whl", hash = "sha256:eecefdce1e5bbfb7ad2eeaabf7c1eeb404d7757c379bd1f7e5cce9d8bf425384"},
@@ -4841,14 +4953,14 @@ files = [
 
 [[package]]
 name = "soupsieve"
-version = "2.4.1"
+version = "2.5"
 description = "A modern CSS selector implementation for Beautiful Soup."
 category = "main"
-optional = false
-python-versions = ">=3.7"
+optional = true
+python-versions = ">=3.8"
 files = [
-    {file = "soupsieve-2.4.1-py3-none-any.whl", hash = "sha256:1c1bfee6819544a3447586c889157365a27e10d88cde3ad3da0cf0ddf646feb8"},
-    {file = "soupsieve-2.4.1.tar.gz", hash = "sha256:89d12b2d5dfcd2c9e8c22326da9d9aa9cb3dfab0a83a024f05704076ee8d35ea"},
+    {file = "soupsieve-2.5-py3-none-any.whl", hash = "sha256:eaa337ff55a1579b6549dc679565eac1e3d000563bcb1c8ab0d0fefbc0c2cdc7"},
+    {file = "soupsieve-2.5.tar.gz", hash = "sha256:5663d5a7b3bfaeee0bc4372e7fc48f9cff4940b3eec54a6451cc5299f1097690"},
 ]
 
 [[package]]
@@ -4876,21 +4988,21 @@ tox = ["dask[array]", "pytest (>=3.5)", "pytest-black", "pytest-cov", "tox"]
 
 [[package]]
 name = "sphinx"
-version = "6.2.1"
+version = "7.1.2"
 description = "Python documentation generator"
 category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "Sphinx-6.2.1.tar.gz", hash = "sha256:6d56a34697bb749ffa0152feafc4b19836c755d90a7c59b72bc7dfd371b9cc6b"},
-    {file = "sphinx-6.2.1-py3-none-any.whl", hash = "sha256:97787ff1fa3256a3eef9eda523a63dbf299f7b47e053cfcf684a1c2a8380c912"},
+    {file = "sphinx-7.1.2-py3-none-any.whl", hash = "sha256:d170a81825b2fcacb6dfd5a0d7f578a053e45d3f2b153fecc948c37344eb4cbe"},
+    {file = "sphinx-7.1.2.tar.gz", hash = "sha256:780f4d32f1d7d1126576e0e5ecc19dc32ab76cd24e950228dcf7b1f6d3d9e22f"},
 ]
 
 [package.dependencies]
 alabaster = ">=0.7,<0.8"
 babel = ">=2.9"
 colorama = {version = ">=0.4.5", markers = "sys_platform == \"win32\""}
-docutils = ">=0.18.1,<0.20"
+docutils = ">=0.18.1,<0.21"
 imagesize = ">=1.3"
 importlib-metadata = {version = ">=4.8", markers = "python_version < \"3.10\""}
 Jinja2 = ">=3.0"
@@ -4913,51 +5025,24 @@ test = ["cython", "filelock", "html5lib", "pytest (>=4.6)"]
 
 [[package]]
 name = "sphinx-autodoc-typehints"
-version = "1.22"
+version = "1.24.0"
 description = "Type hints (PEP 484) support for the Sphinx autodoc extension"
 category = "main"
 optional = true
-python-versions = ">=3.7"
-files = [
-    {file = "sphinx_autodoc_typehints-1.22-py3-none-any.whl", hash = "sha256:ef4a8b9d52de66065aa7d3adfabf5a436feb8a2eff07c2ddc31625d8807f2b69"},
-    {file = "sphinx_autodoc_typehints-1.22.tar.gz", hash = "sha256:71fca2d5eee9b034204e4c686ab20b4d8f5eb9409396216bcae6c87c38e18ea6"},
-]
-
-[package.dependencies]
-furo = {version = ">=2022.12.7", optional = true, markers = "extra == \"docs\""}
-sphinx = [
-    {version = ">=5.3"},
-    {version = ">=6.1.3", optional = true, markers = "extra == \"docs\""},
-]
-
-[package.extras]
-docs = ["furo (>=2022.12.7)", "sphinx (>=6.1.3)", "sphinx-autodoc-typehints (>=1.21)"]
-testing = ["covdefaults (>=2.2.2)", "coverage (>=7.0.5)", "diff-cover (>=7.3)", "nptyping (>=2.4.1)", "pytest (>=7.2.1)", "pytest-cov (>=4)", "sphobjinv (>=2.3.1)", "typing-extensions (>=4.4)"]
-type-comment = ["typed-ast (>=1.5.4)"]
-
-[[package]]
-name = "sphinx-autodoc-typehints"
-version = "1.23.0"
-description = "Type hints (PEP 484) support for the Sphinx autodoc extension"
-category = "main"
-optional = true
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "sphinx_autodoc_typehints-1.23.0-py3-none-any.whl", hash = "sha256:ac099057e66b09e51b698058ba7dd76e57e1fe696cd91b54e121d3dad188f91d"},
-    {file = "sphinx_autodoc_typehints-1.23.0.tar.gz", hash = "sha256:5d44e2996633cdada499b6d27a496ddf9dbc95dd1f0f09f7b37940249e61f6e9"},
+    {file = "sphinx_autodoc_typehints-1.24.0-py3-none-any.whl", hash = "sha256:6a73c0c61a9144ce2ed5ef2bed99d615254e5005c1cc32002017d72d69fb70e6"},
+    {file = "sphinx_autodoc_typehints-1.24.0.tar.gz", hash = "sha256:94e440066941bb237704bb880785e2d05e8ae5406c88674feefbb938ad0dc6af"},
 ]
 
 [package.dependencies]
-furo = {version = ">=2022.12.7", optional = true, markers = "extra == \"docs\""}
-sphinx = [
-    {version = ">=5.3"},
-    {version = ">=6.1.3", optional = true, markers = "extra == \"docs\""},
-]
+furo = {version = ">=2023.5.20", optional = true, markers = "extra == \"docs\""}
+sphinx = ">=7.0.1"
 
 [package.extras]
-docs = ["furo (>=2022.12.7)", "sphinx (>=6.1.3)", "sphinx-autodoc-typehints (>=1.23.4)"]
-testing = ["covdefaults (>=2.2.2)", "coverage (>=7.2.2)", "diff-cover (>=7.5)", "nptyping (>=2.5)", "pytest (>=7.2.2)", "pytest-cov (>=4)", "sphobjinv (>=2.3.1)", "typing-extensions (>=4.5)"]
-type-comment = ["typed-ast (>=1.5.4)"]
+docs = ["furo (>=2023.5.20)", "sphinx (>=7.0.1)"]
+numpy = ["nptyping (>=2.5)"]
+testing = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "diff-cover (>=7.5)", "pytest (>=7.3.1)", "pytest-cov (>=4.1)", "sphobjinv (>=2.3.1)", "typing-extensions (>=4.6.3)"]
 
 [[package]]
 name = "sphinx-basic-ng"
@@ -4979,19 +5064,19 @@ docs = ["furo", "ipython", "myst-parser", "sphinx-copybutton", "sphinx-inline-ta
 
 [[package]]
 name = "sphinx-rtd-theme"
-version = "1.2.2"
+version = "1.3.0"
 description = "Read the Docs theme for Sphinx"
 category = "main"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
 files = [
-    {file = "sphinx_rtd_theme-1.2.2-py2.py3-none-any.whl", hash = "sha256:6a7e7d8af34eb8fc57d52a09c6b6b9c46ff44aea5951bc831eeb9245378f3689"},
-    {file = "sphinx_rtd_theme-1.2.2.tar.gz", hash = "sha256:01c5c5a72e2d025bd23d1f06c59a4831b06e6ce6c01fdd5ebfe9986c0a880fc7"},
+    {file = "sphinx_rtd_theme-1.3.0-py2.py3-none-any.whl", hash = "sha256:46ddef89cc2416a81ecfbeaceab1881948c014b1b6e4450b815311a89fb977b0"},
+    {file = "sphinx_rtd_theme-1.3.0.tar.gz", hash = "sha256:590b030c7abb9cf038ec053b95e5380b5c70d61591eb0b552063fbe7c41f0931"},
 ]
 
 [package.dependencies]
 docutils = "<0.19"
-sphinx = ">=1.6,<7"
+sphinx = ">=1.6,<8"
 sphinxcontrib-jquery = ">=4,<5"
 
 [package.extras]
@@ -5128,14 +5213,14 @@ test = ["Sphinx", "pytest", "sqlalchemy", "whoosh"]
 
 [[package]]
 name = "stack-data"
-version = "0.6.2"
+version = "0.6.3"
 description = "Extract data from python stack frames and tracebacks for informative displays"
 category = "main"
 optional = false
 python-versions = "*"
 files = [
-    {file = "stack_data-0.6.2-py3-none-any.whl", hash = "sha256:cbb2a53eb64e5785878201a97ed7c7b94883f48b87bfb0bbe8b623c74679e4a8"},
-    {file = "stack_data-0.6.2.tar.gz", hash = "sha256:32d2dd0376772d01b6cb9fc996f3c8b57a357089dec328ed4b6553d037eaf815"},
+    {file = "stack_data-0.6.3-py3-none-any.whl", hash = "sha256:d5558e0c25a4cb0853cddad3d77da9891a08cb85dd9f9f91b9f8cd66e511e695"},
+    {file = "stack_data-0.6.3.tar.gz", hash = "sha256:836a778de4fec4dcd1dcd89ed8abff8a221f58308462e1c4aa2a3cf30148f0b9"},
 ]
 
 [package.dependencies]
@@ -5190,7 +5275,7 @@ name = "terminado"
 version = "0.17.1"
 description = "Tornado websocket backend for the Xterm.js Javascript terminal emulator library."
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.7"
 files = [
     {file = "terminado-0.17.1-py3-none-any.whl", hash = "sha256:8650d44334eba354dd591129ca3124a6ba42c3d5b70df5051b6921d506fdaeae"},
@@ -5241,7 +5326,7 @@ name = "tinycss2"
 version = "1.2.1"
 description = "A tiny CSS parser"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.7"
 files = [
     {file = "tinycss2-1.2.1-py3-none-any.whl", hash = "sha256:2b80a96d41e7c3914b8cda8bc7f705a4d9c49275616e886103dd839dfc847847"},
@@ -5293,23 +5378,23 @@ files = [
 
 [[package]]
 name = "tornado"
-version = "6.3.2"
+version = "6.3.3"
 description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
 category = "main"
 optional = false
 python-versions = ">= 3.8"
 files = [
-    {file = "tornado-6.3.2-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:c367ab6c0393d71171123ca5515c61ff62fe09024fa6bf299cd1339dc9456829"},
-    {file = "tornado-6.3.2-cp38-abi3-macosx_10_9_x86_64.whl", hash = "sha256:b46a6ab20f5c7c1cb949c72c1994a4585d2eaa0be4853f50a03b5031e964fc7c"},
-    {file = "tornado-6.3.2-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c2de14066c4a38b4ecbbcd55c5cc4b5340eb04f1c5e81da7451ef555859c833f"},
-    {file = "tornado-6.3.2-cp38-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:05615096845cf50a895026f749195bf0b10b8909f9be672f50b0fe69cba368e4"},
-    {file = "tornado-6.3.2-cp38-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5b17b1cf5f8354efa3d37c6e28fdfd9c1c1e5122f2cb56dac121ac61baa47cbe"},
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-    {file = "tornado-6.3.2-cp38-abi3-win_amd64.whl", hash = "sha256:0c325e66c8123c606eea33084976c832aa4e766b7dff8aedd7587ea44a604cdf"},
-    {file = "tornado-6.3.2.tar.gz", hash = "sha256:4b927c4f19b71e627b13f3db2324e4ae660527143f9e1f2e2fb404f3a187e2ba"},
+    {file = "tornado-6.3.3-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:502fba735c84450974fec147340016ad928d29f1e91f49be168c0a4c18181e1d"},
+    {file = "tornado-6.3.3-cp38-abi3-macosx_10_9_x86_64.whl", hash = "sha256:805d507b1f588320c26f7f097108eb4023bbaa984d63176d1652e184ba24270a"},
+    {file = "tornado-6.3.3-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1bd19ca6c16882e4d37368e0152f99c099bad93e0950ce55e71daed74045908f"},
+    {file = "tornado-6.3.3-cp38-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7ac51f42808cca9b3613f51ffe2a965c8525cb1b00b7b2d56828b8045354f76a"},
+    {file = "tornado-6.3.3-cp38-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:71a8db65160a3c55d61839b7302a9a400074c9c753040455494e2af74e2501f2"},
+    {file = "tornado-6.3.3-cp38-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:ceb917a50cd35882b57600709dd5421a418c29ddc852da8bcdab1f0db33406b0"},
+    {file = "tornado-6.3.3-cp38-abi3-musllinux_1_1_i686.whl", hash = "sha256:7d01abc57ea0dbb51ddfed477dfe22719d376119844e33c661d873bf9c0e4a16"},
+    {file = "tornado-6.3.3-cp38-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:9dc4444c0defcd3929d5c1eb5706cbe1b116e762ff3e0deca8b715d14bf6ec17"},
+    {file = "tornado-6.3.3-cp38-abi3-win32.whl", hash = "sha256:65ceca9500383fbdf33a98c0087cb975b2ef3bfb874cb35b8de8740cf7f41bd3"},
+    {file = "tornado-6.3.3-cp38-abi3-win_amd64.whl", hash = "sha256:22d3c2fa10b5793da13c807e6fc38ff49a4f6e1e3868b0a6f4164768bb8e20f5"},
+    {file = "tornado-6.3.3.tar.gz", hash = "sha256:e7d8db41c0181c80d76c982aacc442c0783a2c54d6400fe028954201a2e032fe"},
 ]
 
 [[package]]
@@ -5335,19 +5420,19 @@ telegram = ["requests"]
 
 [[package]]
 name = "traitlets"
-version = "5.9.0"
+version = "5.11.2"
 description = "Traitlets Python configuration system"
 category = "main"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "traitlets-5.9.0-py3-none-any.whl", hash = "sha256:9e6ec080259b9a5940c797d58b613b5e31441c2257b87c2e795c5228ae80d2d8"},
-    {file = "traitlets-5.9.0.tar.gz", hash = "sha256:f6cde21a9c68cf756af02035f72d5a723bf607e862e7be33ece505abf4a3bad9"},
+    {file = "traitlets-5.11.2-py3-none-any.whl", hash = "sha256:98277f247f18b2c5cabaf4af369187754f4fb0e85911d473f72329db8a7f4fae"},
+    {file = "traitlets-5.11.2.tar.gz", hash = "sha256:7564b5bf8d38c40fa45498072bf4dc5e8346eb087bbf1e2ae2d8774f6a0f078e"},
 ]
 
 [package.extras]
 docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
-test = ["argcomplete (>=2.0)", "pre-commit", "pytest", "pytest-mock"]
+test = ["argcomplete (>=3.0.3)", "mypy (>=1.5.1)", "pre-commit", "pytest (>=7.0,<7.5)", "pytest-mock", "pytest-mypy-testing"]
 
 [[package]]
 name = "traits"
@@ -5425,18 +5510,62 @@ files = [
     {file = "transforms3d-0.4.1.tar.gz", hash = "sha256:31c755266a0b0a222488b8d039f6f325cf486c52728c03e307ce047b2fad1179"},
 ]
 
+[[package]]
+name = "types-python-dateutil"
+version = "2.8.19.14"
+description = "Typing stubs for python-dateutil"
+category = "main"
+optional = true
+python-versions = "*"
+files = [
+    {file = "types-python-dateutil-2.8.19.14.tar.gz", hash = "sha256:1f4f10ac98bb8b16ade9dbee3518d9ace017821d94b057a425b069f834737f4b"},
+    {file = "types_python_dateutil-2.8.19.14-py3-none-any.whl", hash = "sha256:f977b8de27787639986b4e28963263fd0e5158942b3ecef91b9335c130cb1ce9"},
+]
+
 [[package]]
 name = "typing-extensions"
-version = "4.7.1"
-description = "Backported and Experimental Type Hints for Python 3.7+"
+version = "4.8.0"
+description = "Backported and Experimental Type Hints for Python 3.8+"
+category = "main"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "typing_extensions-4.8.0-py3-none-any.whl", hash = "sha256:8f92fc8806f9a6b641eaa5318da32b44d401efaac0f6678c9bc448ba3605faa0"},
+    {file = "typing_extensions-4.8.0.tar.gz", hash = "sha256:df8e4339e9cb77357558cbdbceca33c303714cf861d1eef15e1070055ae8b7ef"},
+]
+
+[[package]]
+name = "tzdata"
+version = "2023.3"
+description = "Provider of IANA time zone data"
+category = "main"
+optional = false
+python-versions = ">=2"
+files = [
+    {file = "tzdata-2023.3-py2.py3-none-any.whl", hash = "sha256:7e65763eef3120314099b6939b5546db7adce1e7d6f2e179e3df563c70511eda"},
+    {file = "tzdata-2023.3.tar.gz", hash = "sha256:11ef1e08e54acb0d4f95bdb1be05da659673de4acbd21bf9c69e94cc5e907a3a"},
+]
+
+[[package]]
+name = "tzlocal"
+version = "4.3"
+description = "tzinfo object for the local timezone"
 category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "typing_extensions-4.7.1-py3-none-any.whl", hash = "sha256:440d5dd3af93b060174bf433bccd69b0babc3b15b1a8dca43789fd7f61514b36"},
-    {file = "typing_extensions-4.7.1.tar.gz", hash = "sha256:b75ddc264f0ba5615db7ba217daeb99701ad295353c45f9e95963337ceeeffb2"},
+    {file = "tzlocal-4.3-py3-none-any.whl", hash = "sha256:b44c4388f3d34f25862cfbb387578a4d70fec417649da694a132f628a23367e2"},
+    {file = "tzlocal-4.3.tar.gz", hash = "sha256:3f21d09e1b2aa9f2dacca12da240ca37de3ba5237a93addfd6d593afe9073355"},
 ]
 
+[package.dependencies]
+"backports.zoneinfo" = {version = "*", markers = "python_version < \"3.9\""}
+pytz-deprecation-shim = "*"
+tzdata = {version = "*", markers = "platform_system == \"Windows\""}
+
+[package.extras]
+devenv = ["black", "check-manifest", "flake8", "pyroma", "pytest (>=4.3)", "pytest-cov", "pytest-mock (>=3.3)", "zest.releaser"]
+
 [[package]]
 name = "uncertainties"
 version = "3.1.7"
@@ -5463,7 +5592,7 @@ name = "uri-template"
 version = "1.3.0"
 description = "RFC 6570 URI Template Processor"
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.7"
 files = [
     {file = "uri-template-1.3.0.tar.gz", hash = "sha256:0e00f8eb65e18c7de20d595a14336e9f337ead580c70934141624b6d1ffdacc7"},
@@ -5490,14 +5619,14 @@ six = "*"
 
 [[package]]
 name = "urllib3"
-version = "2.0.4"
+version = "2.0.6"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "urllib3-2.0.4-py3-none-any.whl", hash = "sha256:de7df1803967d2c2a98e4b11bb7d6bd9210474c46e8a0401514e3a42a75ebde4"},
-    {file = "urllib3-2.0.4.tar.gz", hash = "sha256:8d22f86aae8ef5e410d4f539fde9ce6b2113a001bb4d189e0aed70642d602b11"},
+    {file = "urllib3-2.0.6-py3-none-any.whl", hash = "sha256:7a7c7003b000adf9e7ca2a377c9688bbc54ed41b985789ed576570342a375cd2"},
+    {file = "urllib3-2.0.6.tar.gz", hash = "sha256:b19e1a85d206b56d7df1d5e683df4a7725252a964e3993648dd0fb5a1c157564"},
 ]
 
 [package.extras]
@@ -5508,14 +5637,14 @@ zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
 name = "wcwidth"
-version = "0.2.6"
+version = "0.2.8"
 description = "Measures the displayed width of unicode strings in a terminal"
 category = "main"
 optional = false
 python-versions = "*"
 files = [
-    {file = "wcwidth-0.2.6-py2.py3-none-any.whl", hash = "sha256:795b138f6875577cd91bba52baf9e445cd5118fd32723b460e30a0af30ea230e"},
-    {file = "wcwidth-0.2.6.tar.gz", hash = "sha256:a5220780a404dbe3353789870978e472cfe477761f06ee55077256e509b156d0"},
+    {file = "wcwidth-0.2.8-py2.py3-none-any.whl", hash = "sha256:77f719e01648ed600dfa5402c347481c0992263b81a027344f3e1ba25493a704"},
+    {file = "wcwidth-0.2.8.tar.gz", hash = "sha256:8705c569999ffbb4f6a87c6d1b80f324bd6db952f5eb0b95bc07517f4c1813d4"},
 ]
 
 [[package]]
@@ -5523,7 +5652,7 @@ name = "webcolors"
 version = "1.13"
 description = "A library for working with the color formats defined by HTML and CSS."
 category = "main"
-optional = false
+optional = true
 python-versions = ">=3.7"
 files = [
     {file = "webcolors-1.13-py3-none-any.whl", hash = "sha256:29bc7e8752c0a1bd4a1f03c14d6e6a72e93d82193738fa860cbff59d0fcc11bf"},
@@ -5539,7 +5668,7 @@ name = "webencodings"
 version = "0.5.1"
 description = "Character encoding aliases for legacy web content"
 category = "main"
-optional = false
+optional = true
 python-versions = "*"
 files = [
     {file = "webencodings-0.5.1-py2.py3-none-any.whl", hash = "sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78"},
@@ -5548,36 +5677,33 @@ files = [
 
 [[package]]
 name = "websocket-client"
-version = "1.6.1"
+version = "1.6.3"
 description = "WebSocket client for Python with low level API options"
 category = "main"
-optional = false
-python-versions = ">=3.7"
+optional = true
+python-versions = ">=3.8"
 files = [
-    {file = "websocket-client-1.6.1.tar.gz", hash = "sha256:c951af98631d24f8df89ab1019fc365f2227c0892f12fd150e935607c79dd0dd"},
-    {file = "websocket_client-1.6.1-py3-none-any.whl", hash = "sha256:f1f9f2ad5291f0225a49efad77abf9e700b6fef553900623060dad6e26503b9d"},
+    {file = "websocket-client-1.6.3.tar.gz", hash = "sha256:3aad25d31284266bcfcfd1fd8a743f63282305a364b8d0948a43bd606acc652f"},
+    {file = "websocket_client-1.6.3-py3-none-any.whl", hash = "sha256:6cfc30d051ebabb73a5fa246efdcc14c8fbebbd0330f8984ac3bb6d9edd2ad03"},
 ]
 
 [package.extras]
-docs = ["Sphinx (>=3.4)", "sphinx-rtd-theme (>=0.5)"]
+docs = ["Sphinx (>=6.0)", "sphinx-rtd-theme (>=1.1.0)"]
 optional = ["python-socks", "wsaccel"]
 test = ["websockets"]
 
 [[package]]
 name = "widgetsnbextension"
-version = "3.6.5"
-description = "IPython HTML widgets for Jupyter"
+version = "4.0.9"
+description = "Jupyter interactive widgets for Jupyter Notebook"
 category = "main"
 optional = false
-python-versions = "*"
+python-versions = ">=3.7"
 files = [
-    {file = "widgetsnbextension-3.6.5-py2.py3-none-any.whl", hash = "sha256:a5ae5e8ac0a85b9c527bee8ecb60b3e4b9ce499ed2900c9335b4d94e59f2e086"},
-    {file = "widgetsnbextension-3.6.5.tar.gz", hash = "sha256:601352aa4df7b15717fc57ab59fd9f2795003e53c477dca0d53171c4ee933b35"},
+    {file = "widgetsnbextension-4.0.9-py3-none-any.whl", hash = "sha256:91452ca8445beb805792f206e560c1769284267a30ceb1cec9f5bcc887d15175"},
+    {file = "widgetsnbextension-4.0.9.tar.gz", hash = "sha256:3c1f5e46dc1166dfd40a42d685e6a51396fd34ff878742a3e47c6f0cc4a2a385"},
 ]
 
-[package.dependencies]
-notebook = ">=4.4.1"
-
 [[package]]
 name = "xarray"
 version = "0.20.2"
@@ -5604,23 +5730,24 @@ viz = ["matplotlib", "nc-time-axis", "seaborn"]
 
 [[package]]
 name = "zarr"
-version = "2.16.0"
+version = "2.16.1"
 description = "An implementation of chunked, compressed, N-dimensional arrays for Python"
 category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "zarr-2.16.0-py3-none-any.whl", hash = "sha256:6cf9e6e4c58b9233262e024394e68921a438a6af5a7428bd6bdb1e4e8d05b69a"},
-    {file = "zarr-2.16.0.tar.gz", hash = "sha256:84e36b695bda0ecea52af9861271984cb22a5c864679907b7b9ba3f79b684f7e"},
+    {file = "zarr-2.16.1-py3-none-any.whl", hash = "sha256:de4882433ccb5b42cc1ec9872b95e64ca3a13581424666b28ed265ad76c7056f"},
+    {file = "zarr-2.16.1.tar.gz", hash = "sha256:4276cf4b4a653431042cd53ff2282bc4d292a6842411e88529964504fb073286"},
 ]
 
 [package.dependencies]
 asciitree = "*"
 fasteners = "*"
 numcodecs = ">=0.10.0"
-numpy = ">=1.20"
+numpy = ">=1.20,<1.21.0 || >1.21.0"
 
 [package.extras]
+docs = ["numcodecs[msgpack]", "numpydoc", "pydata-sphinx-theme", "sphinx", "sphinx-copybutton", "sphinx-design", "sphinx-issues", "sphinx-rtd-theme"]
 jupyter = ["ipytree (>=0.2.2)", "ipywidgets (>=8.0.0)", "notebook"]
 
 [[package]]
@@ -5636,18 +5763,18 @@ files = [
 
 [[package]]
 name = "zipp"
-version = "3.16.2"
+version = "3.17.0"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "zipp-3.16.2-py3-none-any.whl", hash = "sha256:679e51dd4403591b2d6838a48de3d283f3d188412a9782faadf845f298736ba0"},
-    {file = "zipp-3.16.2.tar.gz", hash = "sha256:ebc15946aa78bd63458992fc81ec3b6f7b1e92d51c35e6de1c3804e73b799147"},
+    {file = "zipp-3.17.0-py3-none-any.whl", hash = "sha256:0e923e726174922dce09c53c59ad483ff7bbb8e572e00c7f7c46b88556409f31"},
+    {file = "zipp-3.17.0.tar.gz", hash = "sha256:84e64a1c28cf7e91ed2078bb8cc8c259cb19b76942096c8d7b84947690cabaf0"},
 ]
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
 testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"]
 
 [extras]
@@ -5657,4 +5784,4 @@ notebook = ["ipykernel", "jupyter"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8,<3.11"
-content-hash = "9477cb466f2fd33e340ce705e8106180d638586696ce74be39ac2a4232ff6c70"
+content-hash = "d0ffb3199374ca1e88d3f03cc425b5a0cfcc60abac23b1dcd4fdd4f66267492a"
diff --git a/pyproject.toml b/pyproject.toml
index e362ea2a033d16aef03214859b1d70342eb17fa6..45b9b400182c0b154ceb180b0fea84ee3685b0c5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -11,8 +11,8 @@ dask = ">=2021.12.0, <2023.0.0"
 fastdtw = "^0.3.4"
 fastparquet = "^0.8.0"
 h5py = "^3.6.0"
-ipympl = "^0.9.1"
-ipywidgets = "^7.7.1"
+ipympl = ">=0.9.1"
+ipywidgets = ">=7.7.1"
 lmfit = "^1.0.3"
 matplotlib = "^3.5.1"
 natsort = "^8.1.0"
@@ -21,7 +21,7 @@ numpy = ">=1.18,<1.22"
 opencv-python = "<=4.8.0.74"
 pandas = "^1.4.1"
 psutil = "^5.9.0"
-pynxtools = "^0.0.2"
+pynxtools = ">=0.0.2"
 pyyaml = "^6.0.0"
 scipy = "^1.8.0"
 symmetrize = "^0.5.5"
diff --git a/sed/binning/binning.py b/sed/binning/binning.py
index 95cbbbb81bfe75f989e528de64bcbfb993921e17..9a488312fefe895bba2757a15d479123c97107e0 100644
--- a/sed/binning/binning.py
+++ b/sed/binning/binning.py
@@ -46,7 +46,7 @@ def bin_partition(
     Args:
         part (Union[dask.dataframe.DataFrame, pd.DataFrame]): dataframe on which
             to perform the histogram. Usually a partition of a dask DataFrame.
-            bins (int, dict, Sequence[int], Sequence[np.ndarray], Sequence[tuple], optional):
+        bins (int, dict, Sequence[int], Sequence[np.ndarray], Sequence[tuple], optional):
             Definition of the bins. Can  be any of the following cases:
 
                 - an integer describing the number of bins for all dimensions. This
@@ -85,7 +85,7 @@ def bin_partition(
             jitter={'axis':{'amplitude':0.5,'mode':'uniform'}}.
             This example also shows the default behaviour, in case None is
             passed in the dictionary, or jitter is a list of strings.
-            Warning: this is not the most performing approach. applying jitter
+            Warning: this is not the most performing approach. Applying jitter
             on the dataframe before calling the binning is much faster.
             Defaults to None.
         return_edges (bool, optional): If True, returns a list of D arrays
@@ -112,7 +112,8 @@ def bin_partition(
         bins, axes, ranges = simplify_binning_arguments(bins, axes, ranges)
     else:
         if not isinstance(bins, list) or not (
-            all(isinstance(x, int) for x in bins) or all(isinstance(x, np.ndarray) for x in bins)
+            all(isinstance(x, (int, np.int64)) for x in bins)
+            or all(isinstance(x, np.ndarray) for x in bins)
         ):
             raise TypeError(
                 "bins needs to be of type 'List[int] or List[np.ndarray]' if tests are skipped!",
@@ -157,7 +158,7 @@ def bin_partition(
                 mode = jpars.get("mode", "uniform")
                 ax_index = axes.index(col)
                 _bin = bins[ax_index]
-                if isinstance(_bin, int):
+                if isinstance(_bin, (int, np.int64)):
                     rng = ranges[ax_index]
                     binsize = abs(rng[1] - rng[0]) / _bin
                 else:
diff --git a/sed/binning/utils.py b/sed/binning/utils.py
index 195eaa8951f6ef867358449ea1d2841eb1d48ea7..d791c8b8f1c2043274c6490873cd05f3c1a8e616 100644
--- a/sed/binning/utils.py
+++ b/sed/binning/utils.py
@@ -80,7 +80,7 @@ def simplify_binning_arguments(
         bins = bins_
 
     # if bins provided as single int, apply to all dimensions
-    if isinstance(bins, int):
+    if isinstance(bins, (int, np.int64)):
         bins = [bins] * len(axes)
 
     # Check that we have a sequence of bins now
@@ -112,7 +112,7 @@ def simplify_binning_arguments(
         bins = bins_
 
     # if bins are provided as int, check that ranges are present
-    if all(isinstance(x, int) for x in bins):
+    if all(isinstance(x, (int, np.int64)) for x in bins):
         bins = cast(List[int], list(bins))
         if ranges is None:
             raise AttributeError(
diff --git a/sed/calibrator/delay.py b/sed/calibrator/delay.py
index 2b2e2a4dd295dd2b6d31d72db9545b0134250a1b..b0506d6f07c8bb63e27ed45c913bcc9b0a660f66 100644
--- a/sed/calibrator/delay.py
+++ b/sed/calibrator/delay.py
@@ -36,14 +36,8 @@ class DelayCalibrator:
 
         self._config = config
 
-        self.adc_column = self._config.get("dataframe", {}).get(
-            "adc_column",
-            "ADC",
-        )
-        self.delay_column = self._config.get("dataframe", {}).get(
-            "delay_column",
-            "delay",
-        )
+        self.adc_column = self._config["dataframe"]["adc_column"]
+        self.delay_column = self._config["dataframe"]["delay_column"]
         self.calibration: Dict[Any, Any] = {}
 
     def append_delay_axis(
@@ -105,7 +99,7 @@ class DelayCalibrator:
                 calibration = deepcopy(self.calibration)
             else:
                 calibration = deepcopy(
-                    self._config.get("delay", {}).get(
+                    self._config["delay"].get(
                         "calibration",
                         {},
                     ),
@@ -125,23 +119,20 @@ class DelayCalibrator:
         if delay_column is None:
             delay_column = self.delay_column
         if p1_key is None:
-            p1_key = self._config.get("delay", {}).get("p1_key", "")
+            p1_key = self._config["delay"].get("p1_key", "")
         if p2_key is None:
-            p2_key = self._config.get("delay", {}).get("p2_key", "")
+            p2_key = self._config["delay"].get("p2_key", "")
         if t0_key is None:
-            t0_key = self._config.get("delay", {}).get("t0_key", "")
+            t0_key = self._config["delay"].get("t0_key", "")
 
         if "adc_range" not in calibration.keys():
             calibration["adc_range"] = np.asarray(
-                self._config.get("delay", {}).get(
-                    "adc_range",
-                    [2600, 27600],
-                ),
-            ) / 2 ** self._config.get("delay", {}).get("adc_binning", 2)
+                self._config["delay"]["adc_range"],
+            ) / 2 ** (self._config["dataframe"]["adc_binning"] - 1)
 
         if "delay_range" not in calibration.keys():
             if "delay_range_mm" not in calibration.keys() or "time0" not in calibration.keys():
-                if datafile is not None:
+                if datafile is not None and p1_key and p2_key and t0_key:
                     try:
                         ret = extract_delay_stage_parameters(
                             datafile,
diff --git a/sed/calibrator/energy.py b/sed/calibrator/energy.py
index 1a269386a4cd2f0df51dbed56bb17c21c1aab713..8a01fab9981b52562b82197d7076b7b8ed8ae1f5 100644
--- a/sed/calibrator/energy.py
+++ b/sed/calibrator/energy.py
@@ -21,6 +21,7 @@ import matplotlib
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
+import psutil
 import xarray as xr
 from bokeh.io import output_notebook
 from bokeh.palettes import Category10 as ColorCycle
@@ -93,44 +94,22 @@ class EnergyCalibrator:
         self.peaks: np.ndarray = np.asarray([])
         self.calibration: Dict[Any, Any] = {}
 
-        self.tof_column = self._config.get("dataframe", {}).get(
-            "tof_column",
-            "t",
-        )
-        self.corrected_tof_column = self._config.get("dataframe", {}).get(
-            "corrected_tof_column",
-            "t_corrected",
-        )
-        self.energy_column = self._config.get("dataframe", {}).get(
-            "energy_column",
-            "E",
-        )
-        self.x_column = self._config.get("dataframe", {}).get("x_column", "X")
-        self.y_column = self._config.get("dataframe", {}).get("y_column", "Y")
-        self.binwidth: float = self._config.get("dataframe", {}).get(
-            "tof_binwidth",
-            4.125e-12,
-        )
-        self.binning: int = self._config.get("dataframe", {}).get(
-            "tof_binning",
-            1,
-        )
-
-        self.tof_fermi = self._config.get("energy", {}).get(
-            "tof_fermi",
-            132250,
-        ) / 2 ** (self.binning - 1)
-        self.x_width = self._config.get("energy", {}).get("x_width", (-20, 20))
-        self.y_width = self._config.get("energy", {}).get("y_width", (-20, 20))
+        self.tof_column = self._config["dataframe"]["tof_column"]
+        self.corrected_tof_column = self._config["dataframe"]["corrected_tof_column"]
+        self.energy_column = self._config["dataframe"]["energy_column"]
+        self.x_column = self._config["dataframe"]["x_column"]
+        self.y_column = self._config["dataframe"]["y_column"]
+        self.binwidth: float = self._config["dataframe"]["tof_binwidth"]
+        self.binning: int = self._config["dataframe"]["tof_binning"]
+        self.x_width = self._config["energy"]["x_width"]
+        self.y_width = self._config["energy"]["y_width"]
         self.tof_width = np.asarray(
-            self._config.get("energy", {}).get(
-                "tof_width",
-                (-300, 500),
-            ),
+            self._config["energy"]["tof_width"],
         ) / 2 ** (self.binning - 1)
-        self.color_clip = self._config.get("energy", {}).get("color_clip", 300)
+        self.tof_fermi = self._config["energy"]["tof_fermi"] / 2 ** (self.binning - 1)
+        self.color_clip = self._config["energy"]["color_clip"]
 
-        self.correction = self._config.get("energy", {}).get("correction", {})
+        self.correction: Dict[Any, Any] = {}
 
     @property
     def ntraces(self) -> int:
@@ -219,23 +198,20 @@ class EnergyCalibrator:
         if axes is None:
             axes = [self.tof_column]
         if bins is None:
-            bins = [self._config.get("energy", {}).get("bins", 1000)]
+            bins = [self._config["energy"]["bins"]]
         if ranges is None:
             ranges_ = [
-                np.array(
-                    self._config.get("energy", {}).get(
-                        "ranges",
-                        [128000, 138000],
-                    ),
-                )
-                / 2 ** (self.binning - 1),
+                np.array(self._config["energy"]["ranges"]) / 2 ** (self.binning - 1),
             ]
             ranges = [cast(Tuple[float, float], tuple(v)) for v in ranges_]
         # pylint: disable=duplicate-code
         hist_mode = kwds.pop("hist_mode", self._config["binning"]["hist_mode"])
         mode = kwds.pop("mode", self._config["binning"]["mode"])
         pbar = kwds.pop("pbar", self._config["binning"]["pbar"])
-        num_cores = kwds.pop("num_cores", self._config["binning"]["num_cores"])
+        try:
+            num_cores = kwds.pop("num_cores", self._config["binning"]["num_cores"])
+        except KeyError:
+            num_cores = psutil.cpu_count() - 1
         threads_per_worker = kwds.pop(
             "threads_per_worker",
             self._config["binning"]["threads_per_worker"],
@@ -249,7 +225,12 @@ class EnergyCalibrator:
         if biases is None:
             read_biases = True
             if bias_key is None:
-                bias_key = self._config.get("energy", {}).get("bias_key", "")
+                try:
+                    bias_key = self._config["energy"]["bias_key"]
+                except KeyError as exc:
+                    raise ValueError(
+                        "Either Bias Values or a valid bias_key has to be present!",
+                    ) from exc
 
         dataframe, _ = self.loader.read_dataframe(
             files=data_files,
@@ -260,17 +241,23 @@ class EnergyCalibrator:
             bins=bins,
             axes=axes,
             ranges=ranges,
-            histMode=hist_mode,
+            hist_mode=hist_mode,
             mode=mode,
             pbar=pbar,
-            nCores=num_cores,
-            nThreadsPerWorker=threads_per_worker,
-            threadpoolAPI=threadpool_api,
+            n_cores=num_cores,
+            threads_per_worker=threads_per_worker,
+            threadpool_api=threadpool_api,
             return_partitions=True,
             **kwds,
         )
         if read_biases:
-            biases = extract_bias(data_files, bias_key)
+            if bias_key:
+                try:
+                    biases = extract_bias(data_files, bias_key)
+                except KeyError as exc:
+                    raise ValueError(
+                        "Either Bias Values or a valid bias_key has to be present!",
+                    ) from exc
         tof = traces.coords[(axes[0])]
         self.traces = self.traces_normed = np.asarray(traces.T)
         self.tof = np.asarray(tof)
@@ -294,7 +281,147 @@ class EnergyCalibrator:
             order=order,
         )
 
-    def add_features(
+    def adjust_ranges(
+        self,
+        ranges: Tuple,
+        ref_id: int = 0,
+        traces: np.ndarray = None,
+        peak_window: int = 7,
+        apply: bool = False,
+        **kwds,
+    ):
+        """Display a tool to select or extract the equivalent feature ranges
+        (containing the peaks) among all traces.
+
+        Args:
+            ranges (Tuple):
+                Collection of feature detection ranges, within which an algorithm
+                (i.e. 1D peak detector) with look for the feature.
+            ref_id (int, optional): Index of the reference trace. Defaults to 0.
+            traces (np.ndarray, optional): Collection of energy dispersion curves.
+                Defaults to self.traces_normed.
+            peak_window (int, optional): area around a peak to check for other peaks.
+                Defaults to 7.
+            apply (bool, optional): Option to directly apply the provided parameters.
+                Defaults to False.
+            **kwds:
+                keyword arguments for trace alignment (see ``find_correspondence()``).
+        """
+        if traces is None:
+            traces = self.traces_normed
+
+        self.add_ranges(
+            ranges=ranges,
+            ref_id=ref_id,
+            traces=traces,
+            infer_others=True,
+            mode="replace",
+        )
+        self.feature_extract(peak_window=peak_window)
+
+        # make plot
+        labels = kwds.pop("labels", [str(b) + " V" for b in self.biases])
+        figsize = kwds.pop("figsize", (8, 4))
+        plot_segs = []
+        plot_peaks = []
+        fig, ax = plt.subplots(figsize=figsize)
+        colors = plt.get_cmap("rainbow")(np.linspace(0, 1, len(traces)))
+        for itr, color in zip(range(len(traces)), colors):
+            trace = traces[itr, :]
+            # main traces
+            ax.plot(
+                self.tof,
+                trace,
+                ls="-",
+                color=color,
+                linewidth=1,
+                label=labels[itr],
+            )
+            # segments:
+            seg = self.featranges[itr]
+            cond = (self.tof >= seg[0]) & (self.tof <= seg[1])
+            tofseg, traceseg = self.tof[cond], trace[cond]
+            (line,) = ax.plot(
+                tofseg,
+                traceseg,
+                ls="-",
+                color=color,
+                linewidth=3,
+            )
+            plot_segs.append(line)
+            # markers
+            (scatt,) = ax.plot(
+                self.peaks[itr, 0],
+                self.peaks[itr, 1],
+                ls="",
+                marker=".",
+                color="k",
+                markersize=10,
+            )
+            plot_peaks.append(scatt)
+        ax.legend(fontsize=8, loc="upper right")
+        ax.set_title("")
+
+        def update(refid, ranges):
+            self.add_ranges(ranges, refid, traces=traces)
+            self.feature_extract(peak_window=7)
+            for itr, _ in enumerate(self.traces_normed):
+                seg = self.featranges[itr]
+                cond = (self.tof >= seg[0]) & (self.tof <= seg[1])
+                tofseg, traceseg = (
+                    self.tof[cond],
+                    self.traces_normed[itr][cond],
+                )
+                plot_segs[itr].set_ydata(traceseg)
+                plot_segs[itr].set_xdata(tofseg)
+
+                plot_peaks[itr].set_xdata(self.peaks[itr, 0])
+                plot_peaks[itr].set_ydata(self.peaks[itr, 1])
+
+            fig.canvas.draw_idle()
+
+        refid_slider = ipw.IntSlider(
+            value=ref_id,
+            min=0,
+            max=10,
+            step=1,
+        )
+
+        ranges_slider = ipw.IntRangeSlider(
+            value=list(ranges),
+            min=min(self.tof),
+            max=max(self.tof),
+            step=1,
+        )
+
+        update(ranges=ranges, refid=ref_id)
+
+        ipw.interact(
+            update,
+            refid=refid_slider,
+            ranges=ranges_slider,
+        )
+
+        def apply_func(apply: bool):  # pylint: disable=unused-argument
+            self.add_ranges(
+                ranges_slider.value,
+                refid_slider.value,
+                traces=self.traces_normed,
+            )
+            self.feature_extract(peak_window=7)
+            ranges_slider.close()
+            refid_slider.close()
+            apply_button.close()
+
+        apply_button = ipw.Button(description="apply")
+        display(apply_button)  # pylint: disable=duplicate-code
+        apply_button.on_click(apply_func)
+        plt.show()
+
+        if apply:
+            apply_func(True)
+
+    def add_ranges(
         self,
         ranges: Union[List[Tuple], Tuple],
         ref_id: int = 0,
@@ -303,7 +430,7 @@ class EnergyCalibrator:
         mode: str = "replace",
         **kwds,
     ):
-        """Select or extract the equivalent landmarks (e.g. peaks) among all traces.
+        """Select or extract the equivalent feature ranges (containing the peaks) among all traces.
 
         Args:
             ranges (Union[List[Tuple], Tuple]):
@@ -461,6 +588,7 @@ class EnergyCalibrator:
                 aug=self.dup,
                 method=method,
                 t=t,
+                energy_scale=energy_scale,
                 **kwds,
             )
         else:
@@ -526,7 +654,7 @@ class EnergyCalibrator:
                     ax.plot(
                         xaxis + sign * (self.biases[itr] - self.biases[self.calibration["refid"]]),
                         trace,
-                        ls="--",
+                        ls="-",
                         linewidth=1,
                         label=lbs[itr],
                         **linekwds,
@@ -535,7 +663,7 @@ class EnergyCalibrator:
                     ax.plot(
                         xaxis,
                         trace,
-                        ls="--",
+                        ls="-",
                         linewidth=1,
                         label=lbs[itr],
                         **linekwds,
@@ -549,7 +677,7 @@ class EnergyCalibrator:
                     ax.plot(
                         tofseg,
                         traceseg,
-                        color="k",
+                        ls="-",
                         linewidth=2,
                         **linesegkwds,
                     )
@@ -690,7 +818,7 @@ class EnergyCalibrator:
                 calibration = deepcopy(self.calibration)
             else:
                 calibration = deepcopy(
-                    self._config.get("energy", {}).get(
+                    self._config["energy"].get(
                         "calibration",
                         {},
                     ),
@@ -780,7 +908,7 @@ class EnergyCalibrator:
         amplitude: float = None,
         center: Tuple[float, float] = None,
         correction: dict = None,
-        apply=False,
+        apply: bool = False,
         **kwds,
     ):
         """Visualize the energy correction function on top of the TOF/X/Y graphs.
@@ -800,6 +928,8 @@ class EnergyCalibrator:
                 term. Defaults to config["energy"]["correction"]["correction_type"].
             center (Tuple[float, float], optional): Center (x/y) coordinates for the
                 correction. Defaults to config["energy"]["correction"]["center"].
+            correction (dict, optional): Correction dict. Defaults to the config values
+                and is updated from provided and adjusted parameters.
             apply (bool, optional): whether to store the provided parameters within
                 the class. Defaults to False.
             **kwds: Additional parameters to use for the adjustment plots:
@@ -831,23 +961,20 @@ class EnergyCalibrator:
         matplotlib.use("module://ipympl.backend_nbagg")
 
         if correction is None:
-            correction = deepcopy(self.correction)
+            if self.correction:
+                correction = deepcopy(self.correction)
+            else:
+                correction = deepcopy(self._config["energy"].get("correction", {}))
 
         if correction_type is not None:
             correction["correction_type"] = correction_type
 
-        correction_type = correction["correction_type"]
-
         if amplitude is not None:
             correction["amplitude"] = amplitude
 
-        amplitude = correction["amplitude"]
-
         if center is not None:
             correction["center"] = center
 
-        center = correction["center"]
-
         x_column = kwds.pop("x_column", self.x_column)
         y_column = kwds.pop("y_column", self.y_column)
         tof_column = kwds.pop("tof_column", self.tof_column)
@@ -857,27 +984,31 @@ class EnergyCalibrator:
         tof_width = kwds.pop("tof_width", self.tof_width)
         color_clip = kwds.pop("color_clip", self.color_clip)
 
+        correction = {**correction, **kwds}
+
+        if not {"correction_type", "amplitude", "center"}.issubset(set(correction.keys())):
+            raise ValueError(
+                "No valid energy correction found in config and required parameters missing!",
+            )
+
+        if isinstance(correction["center"], list):
+            correction["center"] = tuple(correction["center"])
+
         x = image.coords[x_column].values
         y = image.coords[y_column].values
 
-        x_center = center[0]
-        y_center = center[1]
+        x_center = correction["center"][0]
+        y_center = correction["center"][1]
 
         correction_x = tof_fermi - correction_function(
             x=x,
             y=y_center,
-            correction_type=correction_type,
-            center=center,
-            amplitude=amplitude,
-            **kwds,
+            **correction,
         )
         correction_y = tof_fermi - correction_function(
             x=x_center,
             y=y,
-            correction_type=correction_type,
-            center=center,
-            amplitude=amplitude,
-            **kwds,
+            **correction,
         )
         fig, ax = plt.subplots(2, 1)
         image.loc[
@@ -914,7 +1045,7 @@ class EnergyCalibrator:
         line2 = ax[1].axvline(x=y_center)
 
         amplitude_slider = ipw.FloatSlider(
-            value=amplitude,
+            value=correction["amplitude"],
             min=0,
             max=10,
             step=0.1,
@@ -922,36 +1053,30 @@ class EnergyCalibrator:
         x_center_slider = ipw.FloatSlider(
             value=x_center,
             min=0,
-            max=self._config.get("momentum", {}).get("detector_ranges", [[0, 2048], [0, 2048]])[0][
-                1
-            ],
+            max=self._config["momentum"]["detector_ranges"][0][1],
             step=1,
         )
         y_center_slider = ipw.FloatSlider(
-            value=x_center,
+            value=y_center,
             min=0,
-            max=self._config.get("momentum", {}).get("detector_ranges", [[0, 2048], [0, 2048]])[1][
-                1
-            ],
+            max=self._config["momentum"]["detector_ranges"][1][1],
             step=1,
         )
 
         def update(amplitude, x_center, y_center, **kwds):
+            nonlocal correction
+            correction["amplitude"] = amplitude
+            correction["center"] = (x_center, y_center)
+            correction = {**correction, **kwds}
             correction_x = tof_fermi - correction_function(
                 x=x,
                 y=y_center,
-                correction_type=correction_type,
-                center=(x_center, y_center),
-                amplitude=amplitude,
-                **kwds,
+                **correction,
             )
             correction_y = tof_fermi - correction_function(
                 x=x_center,
                 y=y,
-                correction_type=correction_type,
-                center=(x_center, y_center),
-                amplitude=amplitude,
-                **kwds,
+                **correction,
             )
 
             trace1.set_ydata(correction_x)
@@ -961,10 +1086,24 @@ class EnergyCalibrator:
 
             fig.canvas.draw_idle()
 
-        if correction_type == "spherical":
-            assert "diameter" in correction.keys()
-            diameter = correction["diameter"]
-            update(amplitude, x_center, y_center, diameter=diameter)
+        def common_apply_func(apply: bool):  # pylint: disable=unused-argument
+            self.correction = {}
+            self.correction["amplitude"] = correction["amplitude"]
+            self.correction["center"] = correction["center"]
+            self.correction["correction_type"] = correction["correction_type"]
+            amplitude_slider.close()
+            x_center_slider.close()
+            y_center_slider.close()
+            apply_button.close()
+
+        if correction["correction_type"] == "spherical":
+            try:
+                update(correction["amplitude"], x_center, y_center, diameter=correction["diameter"])
+            except KeyError as exc:
+                raise ValueError(
+                    "Parameter 'diameter' required for correction type 'sperical', ",
+                    "but not present!",
+                ) from exc
 
             diameter_slider = ipw.FloatSlider(
                 value=correction["diameter"],
@@ -981,28 +1120,21 @@ class EnergyCalibrator:
                 diameter=diameter_slider,
             )
 
-            def apply_func(apply: bool):  # pylint: disable=unused-argument
-                self.correction["amplitude"] = amplitude_slider.value
-                self.correction["center"] = (
-                    x_center_slider.value,
-                    y_center_slider.value,
-                )
-                self.correction["correction_type"] = correction_type
-                self.correction["diameter"] = diameter_slider.value
-                amplitude_slider.close()
-                x_center_slider.close()
-                y_center_slider.close()
+            def apply_func(apply: bool):
+                common_apply_func(apply)
+                self.correction["diameter"] = correction["diameter"]
                 diameter_slider.close()
-                apply_button.close()
-
-        elif correction_type == "Lorentzian":
-            assert "gamma" in correction.keys()
-            gamma = correction["gamma"]
 
-            update(amplitude, x_center, y_center, gamma=gamma)
+        elif correction["correction_type"] == "Lorentzian":
+            try:
+                update(correction["amplitude"], x_center, y_center, gamma=correction["gamma"])
+            except KeyError as exc:
+                raise ValueError(
+                    "Parameter 'gamma' required for correction type 'Lorentzian', but not present!",
+                ) from exc
 
             gamma_slider = ipw.FloatSlider(
-                value=gamma,
+                value=correction["gamma"],
                 min=0,
                 max=2000,
                 step=1,
@@ -1016,28 +1148,21 @@ class EnergyCalibrator:
                 gamma=gamma_slider,
             )
 
-            def apply_func(apply: bool):  # pylint: disable=unused-argument
-                self.correction["amplitude"] = amplitude_slider.value
-                self.correction["center"] = (
-                    x_center_slider.value,
-                    y_center_slider.value,
-                )
-                self.correction["correction_type"] = correction_type
-                self.correction["gamma"] = gamma_slider.value
-                amplitude_slider.close()
-                x_center_slider.close()
-                y_center_slider.close()
+            def apply_func(apply: bool):
+                common_apply_func(apply)
+                self.correction["gamma"] = correction["gamma"]
                 gamma_slider.close()
-                apply_button.close()
-
-        elif correction_type == "Gaussian":
-            assert "sigma" in correction.keys()
-            sigma = correction["sigma"]
 
-            update(amplitude, x_center, y_center, sigma=sigma)
+        elif correction["correction_type"] == "Gaussian":
+            try:
+                update(correction["amplitude"], x_center, y_center, sigma=correction["sigma"])
+            except KeyError as exc:
+                raise ValueError(
+                    "Parameter 'sigma' required for correction type 'Gaussian', but not present!",
+                ) from exc
 
             sigma_slider = ipw.FloatSlider(
-                value=sigma,
+                value=correction["sigma"],
                 min=0,
                 max=1000,
                 step=1,
@@ -1051,58 +1176,47 @@ class EnergyCalibrator:
                 sigma=sigma_slider,
             )
 
-            def apply_func(apply: bool):  # pylint: disable=unused-argument
-                self.correction["amplitude"] = amplitude_slider.value
-                self.correction["center"] = (
-                    x_center_slider.value,
-                    y_center_slider.value,
-                )
-                self.correction["correction_type"] = correction_type
-                self.correction["sigma"] = sigma_slider.value
-                amplitude_slider.close()
-                x_center_slider.close()
-                y_center_slider.close()
+            def apply_func(apply: bool):
+                common_apply_func(apply)
+                self.correction["sigma"] = correction["sigma"]
                 sigma_slider.close()
-                apply_button.close()
-
-        elif correction_type == "Lorentzian_asymmetric":
-            assert "gamma" in correction.keys()
-            gamma = correction["gamma"]
-            gamma = kwds.pop("gamma", 700)
-            if "amplitude2" in correction.keys():
-                amplitude2 = correction["amplitude2"]
-            else:
-                amplitude2 = amplitude
-            if "gamma2" in correction.keys():
-                gamma2 = correction["gamma2"]
-            else:
-                gamma2 = gamma
-
-            update(
-                amplitude,
-                x_center,
-                y_center,
-                gamma=gamma,
-                amplitude2=amplitude2,
-                gamma2=gamma2,
-            )
+
+        elif correction["correction_type"] == "Lorentzian_asymmetric":
+            try:
+                if "amplitude2" not in correction:
+                    correction["amplitude2"] = correction["amplitude"]
+                if "sigma2" not in correction:
+                    correction["gamma2"] = correction["gamma"]
+                update(
+                    correction["amplitude"],
+                    x_center,
+                    y_center,
+                    gamma=correction["gamma"],
+                    amplitude2=correction["amplitude2"],
+                    gamma2=correction["gamma2"],
+                )
+            except KeyError as exc:
+                raise ValueError(
+                    "Parameter 'gamma' required for correction type 'Lorentzian_asymmetric', ",
+                    "but not present!",
+                ) from exc
 
             gamma_slider = ipw.FloatSlider(
-                value=gamma,
+                value=correction["gamma"],
                 min=0,
                 max=2000,
                 step=1,
             )
 
             amplitude2_slider = ipw.FloatSlider(
-                value=amplitude,
+                value=correction["amplitude2"],
                 min=0,
                 max=10,
                 step=0.1,
             )
 
             gamma2_slider = ipw.FloatSlider(
-                value=gamma2,
+                value=correction["gamma2"],
                 min=0,
                 max=2000,
                 step=1,
@@ -1118,23 +1232,14 @@ class EnergyCalibrator:
                 gamma2=gamma2_slider,
             )
 
-            def apply_func(apply: bool):  # pylint: disable=unused-argument
-                self.correction["amplitude"] = amplitude_slider.value
-                self.correction["center"] = (
-                    x_center_slider.value,
-                    y_center_slider.value,
-                )
-                self.correction["correction_type"] = correction_type
-                self.correction["gamma"] = gamma_slider.value
-                self.correction["amplitude2"] = amplitude2_slider.value
-                self.correction["gamma2"] = gamma2_slider.value
-                amplitude_slider.close()
-                x_center_slider.close()
-                y_center_slider.close()
+            def apply_func(apply: bool):
+                common_apply_func(apply)
+                self.correction["gamma"] = correction["gamma"]
+                self.correction["amplitude2"] = correction["amplitude2"]
+                self.correction["gamma2"] = correction["gamma2"]
                 gamma_slider.close()
                 amplitude2_slider.close()
                 gamma2_slider.close()
-                apply_button.close()
 
         else:
             raise NotImplementedError
@@ -1202,12 +1307,7 @@ class EnergyCalibrator:
             if self.correction:
                 correction = deepcopy(self.correction)
             else:
-                correction = deepcopy(
-                    self._config.get("energy", {}).get(
-                        "correction",
-                        {},
-                    ),
-                )
+                correction = deepcopy(self._config["energy"].get("correction", {}))
 
         if correction_type is not None:
             correction["correction_type"] = correction_type
@@ -1227,6 +1327,10 @@ class EnergyCalibrator:
         if new_tof_column is None:
             new_tof_column = self.corrected_tof_column
 
+        missing_keys = {"correction_type", "center", "amplitude"} - set(correction.keys())
+        if missing_keys:
+            raise ValueError(f"Required correction parameters '{missing_keys}' missing!")
+
         df[new_tof_column] = df[tof_column] + correction_function(
             x=df[x_column],
             y=df[y_column],
@@ -1309,7 +1413,13 @@ def correction_function(
         float: calculated correction value
     """
     if correction_type == "spherical":
-        diameter = kwds.pop("diameter", 50)
+        try:
+            diameter = kwds.pop("diameter")
+        except KeyError as exc:
+            raise ValueError(
+                f"Parameter 'diameter' required for correction type '{correction_type}' "
+                "but not provided!",
+            ) from exc
         correction = -(
             (
                 1
@@ -1322,7 +1432,13 @@ def correction_function(
         )
 
     elif correction_type == "Lorentzian":
-        gamma = kwds.pop("gamma", 700)
+        try:
+            gamma = kwds.pop("gamma")
+        except KeyError as exc:
+            raise ValueError(
+                f"Parameter 'gamma' required for correction type '{correction_type}' "
+                "but not provided!",
+            ) from exc
         correction = (
             100000
             * amplitude
@@ -1331,7 +1447,13 @@ def correction_function(
         )
 
     elif correction_type == "Gaussian":
-        sigma = kwds.pop("sigma", 400)
+        try:
+            sigma = kwds.pop("sigma")
+        except KeyError as exc:
+            raise ValueError(
+                f"Parameter 'sigma' required for correction type '{correction_type}' "
+                "but not provided!",
+            ) from exc
         correction = (
             20000
             * amplitude
@@ -1345,7 +1467,13 @@ def correction_function(
         )
 
     elif correction_type == "Lorentzian_asymmetric":
-        gamma = kwds.pop("gamma", 700)
+        try:
+            gamma = kwds.pop("gamma")
+        except KeyError as exc:
+            raise ValueError(
+                f"Parameter 'gamma' required for correction type '{correction_type}' "
+                "but not provided!",
+            ) from exc
         gamma2 = kwds.pop("gamma2", gamma)
         amplitude2 = kwds.pop("amplitude2", amplitude)
         correction = (
@@ -1790,6 +1918,7 @@ def poly_energy_calibration(
     t: Union[List[float], np.ndarray] = None,
     aug: int = 1,
     method: str = "lstsq",
+    energy_scale: str = "kinetic",
     **kwds,
 ) -> dict:
     """Energy calibration by nonlinear least squares fitting of spectral landmarks on
@@ -1819,6 +1948,10 @@ def poly_energy_calibration(
             - **'lstsq'**, **'lsqr'**: Energy calibration using polynomial form..
 
             Defaults to "lstsq".
+        energy_scale (str, optional): Direction of increasing energy scale.
+
+            - **'kinetic'**: increasing energy with decreasing TOF.
+            - **'binding'**: increasing energy with increasing TOF.
 
     Returns:
         dict: A dictionary of fitting parameters including the following,
@@ -1877,6 +2010,7 @@ def poly_energy_calibration(
     ecalibdict["coeffs"] = poly_a
     ecalibdict["Tmat"] = t_mat
     ecalibdict["bvec"] = bvec
+    ecalibdict["energy_scale"] = energy_scale
 
     if ref_energy is not None and t is not None:
         energy_offset = pfunc(-1 * ref_energy, pos[ref_id])
diff --git a/sed/calibrator/momentum.py b/sed/calibrator/momentum.py
index 70c85b502e56a1aeece873eb1d924622560f9b82..7fb585de5cf264eb0ccd43c157bb78d80043bf76 100644
--- a/sed/calibrator/momentum.py
+++ b/sed/calibrator/momentum.py
@@ -77,16 +77,15 @@ class MomentumCorrector:
         if data is not None:
             self.load_data(data=data, bin_ranges=bin_ranges)
 
-        self.detector_ranges = self._config.get("momentum", {}).get(
-            "detector_ranges",
-            [[0, 2048], [0, 2048]],
-        )
+        self.detector_ranges = self._config["momentum"]["detector_ranges"]
 
         self.rotsym = int(rotsym)
         self.rotsym_angle = int(360 / self.rotsym)
         self.arot = np.array([0] + [self.rotsym_angle] * (self.rotsym - 1))
         self.ascale = np.array([1.0] * self.rotsym)
         self.peaks: np.ndarray = None
+        self.include_center: bool = False
+        self.use_center: bool = False
         self.pouter: np.ndarray = None
         self.pcent: Tuple[float, ...] = None
         self.pouter_ord: np.ndarray = None
@@ -100,6 +99,8 @@ class MomentumCorrector:
         self.vvdist: float = np.nan
         self.rdeform_field: np.ndarray = None
         self.cdeform_field: np.ndarray = None
+        self.rdeform_field_bkp: np.ndarray = None
+        self.cdeform_field_bkp: np.ndarray = None
         self.inverse_dfield: np.ndarray = None
         self.dfield_updated: bool = False
         self.transformations: Dict[Any, Any] = {}
@@ -107,30 +108,12 @@ class MomentumCorrector:
         self.adjust_params: Dict[Any, Any] = {"applied": False}
         self.calibration: Dict[Any, Any] = {}
 
-        self.x_column = self._config.get("dataframe", {}).get(
-            "x_column",
-            "X",
-        )
-        self.y_column = self._config.get("dataframe", {}).get(
-            "y_column",
-            "Y",
-        )
-        self.corrected_x_column = self._config.get("dataframe", {}).get(
-            "corrected_x_column",
-            "Xm",
-        )
-        self.corrected_y_column = self._config.get("dataframe", {}).get(
-            "corrected_y_column",
-            "Ym",
-        )
-        self.kx_column = self._config.get("dataframe", {}).get(
-            "kx_column",
-            "kx",
-        )
-        self.ky_column = self._config.get("dataframe", {}).get(
-            "ky_column",
-            "ky",
-        )
+        self.x_column = self._config["dataframe"]["x_column"]
+        self.y_column = self._config["dataframe"]["y_column"]
+        self.corrected_x_column = self._config["dataframe"]["corrected_x_column"]
+        self.corrected_y_column = self._config["dataframe"]["corrected_y_column"]
+        self.kx_column = self._config["dataframe"]["kx_column"]
+        self.ky_column = self._config["dataframe"]["ky_column"]
 
         self._state: int = 0
 
@@ -360,26 +343,38 @@ class MomentumCorrector:
         Raises:
             ValueError: Raised if the number of points does not match the rotsym.
         """
+        if features is None:
+            # loading config defauls
+            try:
+                features = np.asarray(
+                    self._config["momentum"]["correction"]["feature_points"],
+                )
+                rotsym = self._config["momentum"]["correction"]["rotation_symmetry"]
+                include_center = self._config["momentum"]["correction"]["include_center"]
+                if not include_center and len(features) > rotsym:
+                    features = features[:rotsym, :]
+            except KeyError as exc:
+                raise ValueError(
+                    "No valid landmarks defined, and no defaults found in configuration!",
+                ) from exc
+
         self.rotsym = int(rotsym)
         self.rotsym_angle = int(360 / self.rotsym)
         self.arot = np.array([0] + [self.rotsym_angle] * (self.rotsym - 1))
         self.ascale = np.array([1.0] * self.rotsym)
 
-        if features is None:
-            features = np.asarray(
-                self._config["momentum"]["correction"]["feature_points"],
-            )
-
         if features.shape[0] == self.rotsym:  # assume no center present
             self.pcent, self.pouter = po.pointset_center(
                 features,
                 method="centroid",
             )
+            self.include_center = False
         elif features.shape[0] == self.rotsym + 1:  # assume center included
             self.pcent, self.pouter = po.pointset_center(
                 features,
                 method="centroidnn",
             )
+            self.include_center = True
         else:
             raise ValueError(
                 f"Found {features.shape[0]} points, ",
@@ -520,8 +515,8 @@ class MomentumCorrector:
             point_input_y.value = point_y
 
         def update_point_pos(
-            point_x: int,
-            point_y: int,
+            point_x: float,
+            point_y: float,
         ):
             fig.canvas.draw_idle()
             point_no = point_no_input.value
@@ -536,8 +531,8 @@ class MomentumCorrector:
             description="Point:",
         )
 
-        point_input_x = ipw.IntText(features[0][0])
-        point_input_y = ipw.IntText(features[0][1])
+        point_input_x = ipw.FloatText(features[0][0])
+        point_input_y = ipw.FloatText(features[0][1])
         ipw.interact(
             update_point_no,
             point_no=point_no_input,
@@ -551,9 +546,7 @@ class MomentumCorrector:
         def onclick(event):
             point_input_x.value = event.xdata
             point_input_y.value = event.ydata
-            point_no_input.value = (point_no_input.value + 1) % features.shape[
-                0
-            ]
+            point_no_input.value = (point_no_input.value + 1) % features.shape[0]
 
         cid = fig.canvas.mpl_connect("button_press_event", onclick)
 
@@ -615,9 +608,10 @@ class MomentumCorrector:
     def spline_warp_estimate(
         self,
         image: np.ndarray = None,
-        include_center: bool = True,
+        use_center: bool = None,
         fixed_center: bool = True,
         interp_order: int = 1,
+        verbose: bool = True,
         **kwds,
     ) -> np.ndarray:
         """Estimate the spline deformation field using thin plate spline registration.
@@ -625,15 +619,17 @@ class MomentumCorrector:
         Args:
             image (np.ndarray, optional):
                 2D array. Image slice to be corrected. Defaults to self.slice.
-            include_center (bool, optional):
-                Option to include the image center/centroid in the registration
-                process. Defaults to True.
+            use_center (bool, optional):
+                Option to use the image center/centroid in the registration
+                process. Defaults to config value, or True.
             fixed_center (bool, optional):
                 Option to have a fixed center during registration-based
                 symmetrization. Defaults to True.
             interp_order (int, optional):
                 Order of interpolation (see ``scipy.ndimage.map_coordinates()``).
                 Defaults to 1.
+            verbose (bool, optional): Option to report the used landmarks for correction.
+                Defaults to True.
             **kwds: keyword arguments:
 
                 - **landmarks**: (list/array): Landmark positions (row, column) used
@@ -661,6 +657,13 @@ class MomentumCorrector:
                 print("No landmarks defined, using config defaults.")
                 self.add_features()
 
+        if use_center is None:
+            try:
+                use_center = self._config["momentum"]["correction"]["use_center"]
+            except KeyError:
+                use_center = True
+        self.use_center = use_center
+
         self.prefs = kwds.pop("landmarks", self.pouter_ord)
         self.ptargs = kwds.pop("targets", [])
 
@@ -675,8 +678,8 @@ class MomentumCorrector:
                 ret="all",
             )[1:, :]
 
-        if include_center is True:
-            # Include center of image pattern in the registration-based symmetrization
+        if use_center is True:
+            # Use center of image pattern in the registration-based symmetrization
             if fixed_center is True:
                 # Add the same center to both the reference and target sets
 
@@ -710,16 +713,27 @@ class MomentumCorrector:
             splinewarp[1],
         )
 
+        # save backup copies to reset transformations
+        self.rdeform_field_bkp = self.rdeform_field
+        self.cdeform_field_bkp = self.cdeform_field
+
         self.correction["applied"] = True
         self.correction["pouter"] = self.pouter_ord
         self.correction["pcent"] = np.asarray(self.pcent)
         self.correction["prefs"] = self.prefs
         self.correction["ptargs"] = self.ptargs
         self.correction["rotsym"] = self.rotsym
+        self.correction["use_center"] = self.use_center
 
         if self.slice is not None:
             self.slice_corrected = corrected_image
 
+        if verbose:
+            print("Calulated thin spline correction based on the following landmarks:")
+            print(f"pouter: {self.pouter}")
+            if use_center:
+                print(f"pcent: {self.pcent}")
+
         return corrected_image
 
     def apply_correction(
@@ -963,6 +977,8 @@ class MomentumCorrector:
         ytrans: float = 0,
         angle: float = 0,
         apply: bool = False,
+        reset: bool = True,
+        verbose: bool = True,
     ):
         """Interactive panel to adjust transformations that are applied to the image.
         Applies first a scaling, next a x/y translation, and last a rotation around
@@ -980,18 +996,26 @@ class MomentumCorrector:
             apply (bool, optional):
                 Option to directly apply the provided transformations.
                 Defaults to False.
+            reset (bool, optional):
+                Option to reset the correction before transformation. Defaults to True.
+            verbose (bool, optional):
+                Option to report the performed transformations. Defaults to True.
         """
         matplotlib.use("module://ipympl.backend_nbagg")
         source_image = self.slice_corrected
 
         transformed_image = source_image
 
+        if reset:
+            if self.rdeform_field_bkp is not None and self.cdeform_field_bkp is not None:
+                self.rdeform_field = self.rdeform_field_bkp
+                self.cdeform_field = self.cdeform_field_bkp
+            else:
+                self.reset_deformation()
+
         fig, ax = plt.subplots(1, 1)
         img = ax.imshow(transformed_image.T, origin="lower", cmap="terrain_r")
-        center = self._config.get("momentum", {}).get(
-            "center_pixel",
-            [256, 256],
-        )
+        center = self._config["momentum"]["center_pixel"]
         ax.axvline(x=center[0])
         ax.axhline(y=center[1])
 
@@ -1050,6 +1074,7 @@ class MomentumCorrector:
             step=1,
         )
         angle_slider = ipw.FloatSlider(value=angle, min=-180, max=180, step=1)
+        results_box = ipw.Output()
         ipw.interact(
             update,
             scale=scale_slider,
@@ -1066,6 +1091,9 @@ class MomentumCorrector:
                     yscale=self.transformations["scale"],
                     keep=True,
                 )
+                if verbose:
+                    with results_box:
+                        print(f"Applied scaling with scale={self.transformations['scale']}.")
             if (
                 self.transformations.get("xtrans", 0) != 0
                 or self.transformations.get("ytrans", 0) != 0
@@ -1076,6 +1104,12 @@ class MomentumCorrector:
                     ytrans=self.transformations["ytrans"],
                     keep=True,
                 )
+                if verbose:
+                    with results_box:
+                        print(
+                            f"Applied translation with (xtrans={self.transformations['xtrans']},",
+                            f"ytrans={self.transformations['ytrans']}).",
+                        )
             if self.transformations.get("angle", 0) != 0:
                 self.coordinate_transform(
                     transform_type="rotation",
@@ -1083,6 +1117,11 @@ class MomentumCorrector:
                     center=center,
                     keep=True,
                 )
+                if verbose:
+                    with results_box:
+                        print(f"Applied rotation with angle={self.transformations['angle']}.")
+
+                display(results_box)
 
             img.set_data(self.slice_transformed.T)
             axmin = np.min(self.slice_transformed, axis=(0, 1))
@@ -1189,10 +1228,7 @@ class MomentumCorrector:
             ax.imshow(image.T, origin=origin, cmap=cmap, **imkwds)
 
             if cross:
-                center = self._config.get("momentum", {}).get(
-                    "center_pixel",
-                    [256, 256],
-                )
+                center = self._config["momentum"]["center_pixel"]
                 ax.axvline(x=center[0])
                 ax.axhline(y=center[1])
 
@@ -1340,10 +1376,7 @@ class MomentumCorrector:
             raise ValueError("No valid image loaded!")
 
         if point_b is None:
-            point_b = self._config.get("momentum", {}).get(
-                "center_pixel",
-                [256, 256],
-            )
+            point_b = self._config["momentum"]["center_pixel"]
 
         if point_a is None:
             point_a = [0, 0]
@@ -1750,7 +1783,7 @@ class MomentumCorrector:
                 calibration = deepcopy(self.calibration)
             else:
                 calibration = deepcopy(
-                    self._config.get("momentum", {}).get(
+                    self._config["momentum"].get(
                         "calibration",
                         {},
                     ),
diff --git a/tests/data/config/NXmpes_config.json b/sed/config/NXmpes_config.json
similarity index 98%
rename from tests/data/config/NXmpes_config.json
rename to sed/config/NXmpes_config.json
index c72fba53e0941129e64b7921947e9cd365605760..dcc5742935cff8e167c104e3f60206df74e4c64a 100755
--- a/tests/data/config/NXmpes_config.json
+++ b/sed/config/NXmpes_config.json
@@ -220,7 +220,7 @@
   "/ENTRY[entry]/SAMPLE[sample]/preparation_description/description": "Here should be a description of the preparation procedure.",
   "/ENTRY[entry]/SAMPLE[sample]/chemical_formula": "@attrs:metadata/sample/chemical_formula",
   "/ENTRY[entry]/SAMPLE[sample]/description": "@attrs:metadata/sample/chemical_formula",
-  "/ENTRY[entry]/SAMPLE[sample]/name": "@attrs:metadata/sample/chemical_formula",
+  "/ENTRY[entry]/SAMPLE[sample]/name": "@attrs:metadata/sample/name",
   "/ENTRY[entry]/SAMPLE[sample]/gas_pressure": "@attrs:metadata/file/trARPES:XGS600:PressureAC:P_RD",
   "/ENTRY[entry]/SAMPLE[sample]/gas_pressure/@units": "mbar",
   "/ENTRY[entry]/SAMPLE[sample]/situation": "vacuum",
diff --git a/sed/config/default.yaml b/sed/config/default.yaml
index 8e08c824d789bc6ef23fbfe7c74e8703eb97d833..85066a040e1e1582d625b27a08620bd38a9c597e 100644
--- a/sed/config/default.yaml
+++ b/sed/config/default.yaml
@@ -1,14 +1,110 @@
 core:
-  loader: dask
+  # The loader to use. The generic loader allows for loading various files types directly using dask.
+  loader: generic
+
+dataframe:
+  # dataframe column containing x coordinates
+  x_column: "X"
+  # dataframe column containing y coordinates
+  y_column: "Y"
+  # dataframe column containing time-of-flight data
+  tof_column: "t"
+  # dataframe column containing analog-to-digital data
+  adc_column: "ADC"
+  # dataframe column containing corrected x coordinates
+  corrected_x_column: "Xm"
+  # dataframe column containing corrected y coordinates
+  corrected_y_column: "Ym"
+  # dataframe column containing corrected time-of-flight data
+  corrected_tof_column: "tm"
+  # dataframe column containing kx coordinates
+  kx_column: "kx"
+  # dataframe column containing ky coordinates
+  ky_column: "ky"
+  # dataframe column containing energy data
+  energy_column: "energy"
+  # dataframe column containing delay data
+  delay_column: "delay"
+  # time length of a base time-of-flight bin in ns
+  tof_binwidth: 4.125e-12
+  # Binning factor of the tof_column-data compared to tof_binwidth (2^(tof_binning-1))
+  tof_binning: 1
+  # binning factor used for the adc coordinate (2^(adc_binning-1))
+  adc_binning: 1
+
+energy:
+  # Number of bins to use for energy calibration traces
+  bins: 1000
+  # Bin ranges to use for energy calibration curves (for tof_binning=0)
+  ranges: [100000, 150000]
+  # Option to normalize energy calibration traces
+  normalize: True
+  # Pixel range for smoothing
+  normalize_span: 7
+  # Spline order for smoothing
+  normalize_order: 1
+  # Radius parameter for fastdtw algorithm to find path correspondence
+  fastdtw_radius: 2
+  # Window around a peak to make sure that no other peaks are present
+  peak_window: 7
+  # Mehtod to use for energy calibration
+  calibration_method: "lmfit"
+  # Energy scale to use for energy calibration
+  energy_scale: "kinetic"
+  # Approximate position of the high-energy-cutoff in tof_column bins,
+  # used for displaying a graph to choose the energy correction function parameters.
+  tof_fermi: 132250
+  # TOF range to visualize for the correction tool around tof_fermi
+  tof_width: [-600, 1000]
+  # x-intergration range for the correction tool around the center pixel
+  x_width: [-20, 20]
+  # y-intergration range for the correction tool around the center pixel
+  y_width: [-20, 20]
+  # High intensity cutoff for the visulaization tool
+  color_clip: 300
+
+
+momentum:
+  # binning axes to use for momentum correction/calibration.
+  # Axes names starting with "@" refer to keys in the "dataframe" section
+  axes: ["@x_column", "@y_column", "@tof_column"]
+  # Bin numbers used for the respective axes
+  bins: [512, 512, 300]
+  # bin ranges to use (in unbinned detector coordinates)
+  ranges: [[-256, 1792], [-256, 1792], [132000, 138000]]
+  # The x/y pixel ranges of the detector
+  detector_ranges: [[0, 2048], [0, 2048]]
+  # The center pixel of the detector in the binned x/y coordinates
+  center_pixel: [256, 256]
+  # Sigma parameter for feature selection (intensity above background)
+  sigma: 5
+  # FWHM parameter for feature selection (width of features to extract)
+  fwhm: 8
+  # Sigma_radius parameter for feature selection (variation of radius size)
+  sigma_radius: 1
+
+delay:
+  # value ranges of the analog-to-digital converter axes used for encoding the delay stage position
+  # (in unbinned coordinates)
+  adc_range: [1900, 25600]
 
 binning:
+  # Histogram computation mode to use.
   hist_mode: "numba"
+  # Mode for hostogram recombination to use
   mode: fast
+  # Whether to display a progress bar
   pbar: True
+  # Number of multithreading threads per worker thread
   threads_per_worker: 4
+  # API for numpy multithreading
   threadpool_API: "blas"
 
 histogram:
-  bins: [80, 80, 80, 80]
-  axes: ["X", "Y", "t", "ADC"]
-  ranges: [[0, 1800], [0, 1800], [65000, 69000], [0, 500], ]
+  # number of bins used for histogram visualization
+  bins: [80, 80, 80]
+  # default axes to use for histgram visualization.
+  # Axes names starting with "@" refer to keys in the "dataframe" section
+  axes: ["@x_column", "@y_column", "@tof_column"]
+  # default ranges to use for histogram visualization (in unbinned detector coordinates)
+  ranges: [[0, 1800], [0, 1800], [0, 150000]]
diff --git a/sed/config/flash_example_config.yaml b/sed/config/flash_example_config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..9a6149758b2b96272c5968874c272d8ef9bf7606
--- /dev/null
+++ b/sed/config/flash_example_config.yaml
@@ -0,0 +1,98 @@
+# This file contains the default configuration for the flash loader.
+
+core:
+  # defines the loader
+  loader: flash
+
+  # the beamline where experiment took place
+  beamline: pg2
+
+  # The paths to the raw and parquet data directories.
+  paths:
+    data_raw_dir: "tests/data/loader/flash/"
+    data_parquet_dir: "tests/data/loader/flash/parquet"
+    
+  # These can be replaced by beamtime_id and year to automatically
+  # find the folders on the desy cluster
+  
+  # beamtime_id: xxxxxxxx
+  # year: 20xx
+
+dataframe:
+  # The offset correction to the pulseId
+  ubid_offset: 5
+
+  # The name of the DAQ system to use. Necessary to resolve the filenames/paths.
+  daq: fl1user3
+
+  # The channels to load.
+  # channels have the following structure:
+  # channelAlias:
+  #   format: per_pulse/per_electron/per_train
+  #   group_name: the hdf5 group path
+  #   slice: if the group contains multidim data, where to slice
+  
+  channels:
+    # pulse ID is a necessary channel for using the loader.
+    pulseId:
+      format: per_electron
+      group_name: "/uncategorised/FLASH.EXP/HEXTOF.DAQ/DLD1/"
+      slice: 2
+
+    dldPosX:
+      format: per_electron
+      group_name: "/uncategorised/FLASH.EXP/HEXTOF.DAQ/DLD1/"
+      slice: 1
+      
+    dldPosY:
+      format: per_electron
+      group_name: "/uncategorised/FLASH.EXP/HEXTOF.DAQ/DLD1/"
+      slice: 0
+      
+    dldTime:
+      format: per_electron
+      group_name: "/uncategorised/FLASH.EXP/HEXTOF.DAQ/DLD1/"
+      slice: 3
+      
+    # The auxillary channel has a special structure where the group further contains
+    # a multidim structure so further aliases are defined below
+    dldAux:
+      format: per_pulse
+      group_name: "/uncategorised/FLASH.EXP/HEXTOF.DAQ/DLD1/"
+      slice: 4
+      dldAuxChannels:
+        sampleBias: 0
+        tofVoltage: 1
+        extractorVoltage: 2
+        extractorCurrent: 3
+        cryoTemperature: 4
+        sampleTemperature: 5
+        dldTimeBinSize: 15
+        
+  # The prefixes of the stream names for different DAQ systems for parsing filenames
+  # (Not to be changed by user)
+  stream_name_prefixes:
+    pbd: "GMD_DATA_gmd_data"
+    pbd2: "FL2PhotDiag_pbd2_gmd_data"
+    fl1user1: "FLASH1_USER1_stream_2"
+    fl1user2: "FLASH1_USER2_stream_2"
+    fl1user3: "FLASH1_USER3_stream_2"
+    fl2user1: "FLASH2_USER1_stream_2"
+    fl2user2: "FLASH2_USER2_stream_2"
+
+  # The beamtime directories for different DAQ systems.
+  # (Not to be changed by user)
+  beamtime_dir:
+    pg2: "/asap3/flash/gpfs/pg2/"
+
+# metadata collection from scicat
+# metadata:
+#   scicat_url: <URL>
+#   scicat_username: <username>
+#   scicat_password: <password>
+
+# The nexus collection routine shall be finalized soon for both instruments
+# nexus:
+#   reader: "flash"
+#   definition: "NXmpes"
+#   input_files: ["NXmpes_config_HEXTOF_light.json"]
diff --git a/sed/config/mpes_example_config.yaml b/sed/config/mpes_example_config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..ba46bfec0487c06b1b290f2e74923339d2cae8dd
--- /dev/null
+++ b/sed/config/mpes_example_config.yaml
@@ -0,0 +1,308 @@
+core:
+  # The loader to use. The mpes loader allows for loading hdf5 files from the METIS momentum microscope.
+  loader: mpes
+  # Option to use the copy tool to mirror data to a local storage location before processing.
+  use_copy_tool: False
+  # path to the root of the source data directory
+  copy_tool_source: "/path/to/data/"
+  # path to the root or the local data storage
+  copy_tool_dest: "/path/to/localDataStore/"
+  # optional keyworkds for the copy tool:
+  copy_tool_kwds:
+    # number of parallel copy jobs
+    ntasks: 20
+    # group id to set for copied files and folders
+    gid: 1001
+
+dataframe:
+  # hdf5 group names to read from the h5 files (for mpes reader)
+  hdf5_groupnames: ["Stream_0", "Stream_1", "Stream_2", "Stream_4"]
+  # aliases to assign to the dataframe columns for the corresponding hdf5 streams
+  hdf5_aliases:
+    Stream_0: "X"
+    Stream_1: "Y"
+    Stream_2: "t"
+    Stream_4: "ADC"
+  # dataframe column name for the time stamp column
+  time_stamp_alias: "timeStamps"
+  # hdf5 group name containing eventIDs occuring at every millisecond (used to calculate timestamps)
+  ms_markers_group: "msMarkers"
+  # hdf5 attribute containing the timestamp of the first event in a file
+  first_event_time_stamp_key: "FirstEventTimeStamp"
+  # list of columns to apply jitter to
+  jitter_cols: ["X", "Y", "t", "ADC"]
+  # dataframe column containing x coordinates
+  x_column: "X"
+  # dataframe column containing y coordinates
+  y_column: "Y"
+  # dataframe column containing time-of-flight data
+  tof_column: "t"
+  # dataframe column containing analog-to-digital data
+  adc_column: "ADC"
+  # dataframe column containing corrected x coordinates
+  corrected_x_column: "Xm"
+  # dataframe column containing corrected y coordinates
+  corrected_y_column: "Ym"
+  # dataframe column containing corrected time-of-flight data
+  corrected_tof_column: "tm"
+  # dataframe column containing kx coordinates
+  kx_column: "kx"
+  # dataframe column containing ky coordinates
+  ky_column: "ky"
+  # dataframe column containing energy data
+  energy_column: "energy"
+  # dataframe column containing delay data
+  delay_column: "delay"
+  # time length of a base time-of-flight bin in ns
+  tof_binwidth: 4.125e-12
+  # Binning factor of the tof_column-data compared to tof_binwidth (2^(tof_binning-1))
+  tof_binning: 2
+  # binning factor used for the adc coordinate (2^(adc_binning-1))
+  adc_binning: 3
+  # Default units for dataframe entries
+  units:
+    X: 'step'
+    Y: 'step'
+    t: 'step'
+    tof_voltage: 'V'
+    extractor_voltage: 'V'
+    extractor_current: 'A'
+    cryo_temperature: 'K'
+    sample_temperature: 'K'
+    dld_time: 'ns'
+    delay: 'ps'
+    timeStamp: 's'
+    energy: 'eV'
+    E: 'eV'
+    kx: '1/A'
+    ky: '1/A'
+
+energy:
+  # Number of bins to use for energy calibration traces
+  bins: 1000
+  # Bin ranges to use for energy calibration curves (for tof_binning=0)
+  ranges: [128000, 138000]
+  # hdf5 path to attribute storing bias information for a given file
+  bias_key: "@KTOF:Lens:Sample:V"
+  # Option to normalize energy calibration traces
+  normalize: True
+  # Pixel range for smoothing
+  normalize_span: 7
+  # Spline order for smoothing
+  normalize_order: 1
+  # Radius parameter for fastdtw algorithm to find path correspondence
+  fastdtw_radius: 2
+  # Window around a peak to make sure that no other peaks are present
+  peak_window: 7
+  # Mehtod to use for energy calibration
+  calibration_method: "lmfit"
+  # Energy scale to use for energy calibration
+  energy_scale: "kinetic"
+  # Approximate position of the high-energy-cutoff in tof_column bins,
+  # used for displaying a graph to choose the energy correction function parameters.
+  tof_fermi: 132250
+  # TOF range to visualize for the correction tool around tof_fermi
+  tof_width: [-600, 1000]
+  # x-intergration range for the correction tool around the center pixel
+  x_width: [-20, 20]
+  # y-intergration range for the correction tool around the center pixel
+  y_width: [-20, 20]
+  # High intensity cutoff for the visulaization tool
+  color_clip: 300
+  correction:
+    # Correction type
+    correction_type: "Lorentzian"
+    # Correction amplitude
+    amplitude: 2.5
+    # center coordinates for the correction (in detector coordinates)
+    center: [730.0, 730.0]
+    # gamma value for the Lorentzian correction (same for x and y)
+    gamma: 920.0
+    # sigma value for the gaussian correction (same for x and y)
+    sigma: 700.0
+    # diameter value for the radial correction (same for x and y)
+    diameter: 3000.0
+  # Default energy calibration
+  calibration:
+    # time-of-flight distance (in m)
+    d: 1.058206295066418
+    # time offset (in ns)
+    t0: 7.684410678887588e-07
+    # energy offset (in eV)
+    E0: -30.440035779171833
+    # energy scale of calibration
+    energy_scale: "kinetic"
+
+momentum:
+  # binning axes to use for momentum correction/calibration.
+  # Axes names starting with "@" refer to keys in the "dataframe" section
+  axes: ["@x_column", "@y_column", "@tof_column"]
+  # Bin numbers used for the respective axes
+  bins: [512, 512, 300]
+  # bin ranges to use (in unbinned detector coordinates)
+  ranges: [[-256, 1792], [-256, 1792], [132000, 136000]]
+  # The x/y pixel ranges of the detector
+  detector_ranges: [[0, 2048], [0, 2048]]
+  # The center pixel of the detector in the binned x/y coordinates
+  center_pixel: [256, 256]
+  # Sigma parameter for feature selection (intensity above background)
+  sigma: 5
+  # FWHM parameter for feature selection (width of features to extract)
+  fwhm: 8
+  # Sigma_radius parameter for feature selection (variation of radius size)
+  sigma_radius: 1
+  # default momentum calibration
+  calibration:
+    # x momentum scaleing factor
+    kx_scale: 0.012389400615413859
+    # y momentum scaleing factor
+    ky_scale: 0.012389400615413859
+    # x BZ center pixel
+    x_center: 256.0
+    # y BZ center pixel
+    y_center: 256.0
+    # x start value of the calibration dataset
+    rstart: -256.
+    # y start value of the calibration dataset
+    cstart: -256.
+    # x direction pixel stepping of the calibration dataset
+    rstep: 4.0
+    # y direction pixel stepping of the calibration dataset
+    cstep: 4.0
+  correction:
+    # default feature points used for calculating the distortion correction.
+    feature_points: [[203.2, 341.96], [299.16, 345.32], [350.25, 243.70], [304.38, 149.88], [199.52, 152.48], [154.28, 242.27], [248.29, 248.62]]
+    # rotational symmetry of the structure used for correction. Should be an even number
+    rotation_symmetry: 6
+    # Option whether the center of the structure is included in the feature points.
+    include_center: True
+    # Option whether the center should be included in the correction algorithm
+    use_center: True
+
+delay:
+  # value ranges of the analog-to-digital converter axes used for encoding the delay stage position
+  # (in unbinned coordinates)
+  adc_range: [1900, 25600]
+  # hdf5 attribute containing the starting point of the delay stage
+  p1_key: "@trARPES:DelayStage:p1"
+  # hdf5 attribute containing the end point of the delay stage
+  p2_key: "@trARPES:DelayStage:p2"
+  # hdf5 attribute containing the t0 value of the delay stage
+  t0_key: "@trARPES:DelayStage:t0"
+
+binning:
+  # Histogram computation mode to use.
+  hist_mode: "numba"
+  # Mode for hostogram recombination to use
+  mode: "fast"
+  # Whether to display a progress bar
+  pbar: True
+  # Number of parallel binning threads to use
+  num_cores: 20
+  # Number of multithreading threads per worker thread
+  threads_per_worker: 4
+  # API for numpy multithreading
+  threadpool_API: "blas"
+
+histogram:
+  # number of bins used for histogram visualization
+  bins: [80, 80, 80, 80]
+  # default axes to use for histgram visualization.
+  # Axes names starting with "@" refer to keys in the "dataframe" section
+  axes: ["@x_column", "@y_column", "@tof_column", "@adc_column"]
+  # default ranges to use for histogram visualization (in unbinned detector coordinates)
+  ranges: [[0, 1800], [0, 1800], [128000, 138000], [0, 32000]]
+
+metadata:
+  # EPICS channels to collect from EPICS archiver
+  epics_pvs: ["KTOF:Lens:Extr:I", "trARPES:Carving:TEMP_RBV", "trARPES:XGS600:PressureAC:P_RD", "KTOF:Lens:UDLD:V", "KTOF:Lens:Sample:V", "KTOF:Apertures:m1.RBV", "KTOF:Apertures:m2.RBV", "KTOF:Apertures:m3.RBV", "trARPES:Carving:TRX.RBV", "trARPES:Carving:TRY.RBV", "trARPES:Carving:TRZ.RBV", "trARPES:Carving:THT.RBV", "trARPES:Carving:PHI.RBV", "trARPES:Carving:OMG.RBV"]
+  # hdf5 attribute containing the field aperture "in" motor position
+  fa_in_channel: 'KTOF:Apertures:m1.RBV'
+  # hdf5 attribute containing the field aperture "hor" motor position
+  fa_hor_channel: 'KTOF:Apertures:m2.RBV'
+  # hdf5 attribute containing the contrast aperture "in" motor position
+  ca_in_channel: 'KTOF:Apertures:m3.RBV'
+  # dictionary containing contrast and field aperture motor positions and sizes
+  aperture_config:
+    "2018-01-23T19:35:15":
+      fa_size:
+        '750': [[-3.0, -1.4], [-5.4, -4.6]]
+        grid: [[-3.0, -1.4], [0.15, 1.75]]
+        '1500': [[-3.0, -1.4], [6.25, 7.75]]
+        '200': [[3.3, 4.4], [-5.4, -4.6]]
+        '500': [[3.3, 4.4], [0.15, 1.75]]
+        '1000': [[3.3, 4.4], [6.25, 7.75]]
+        '20': [[9.6, 10.1], [-5.4, -4.6]]
+        '50': [[9.6, 10.1], [0.15, 1.75]]
+        '100': [[9.6, 10.1], [6.25, 7.75]]
+        open: [[-15, -9.0], [-15, -8.9]]
+      ca_size:
+        '50': [8.0, 8.4]
+        '200': [-0.5, -0.9]
+        '100': [3.4, 3.8]
+        grid: [-5.3, -5.9]
+        open: [-12.0, -8]
+    "2020-01-23T19:35:15":
+      fa_size:
+        '750': [[-6.2, -4.8], [5.0, 6.0]]
+        grid: [[-6.2, -4.8], [-0.7, -0.3]]
+        '500': [[-6.2, -4.8], [-7.0, -6.0]]
+        '200': [[0.5, 0.9], [-0.7, -0.3]]
+        '100': [[0.5, 0.9], [-7.0, -6.0]]
+        '300': [[0.5, 0.9], [5.0, 6.0]]
+        '10': [[6.5, 6.9], [-7.0, -6.0]]
+        '20': [[6.5, 6.9], [-0.7, -0.3]]
+        '50': [[6.5, 6.9], [5.0, 6.0]]
+        open: [[-15, -8.5], [-15, -8.9]]
+      ca_size:
+        '50': [9.0, 11.0]
+        '300': [-0.1, 0.1]
+        '200': [0.7, 1.5]
+        '100': [5.1, 5.9]
+        grid: [-5.5, -5.2]
+        open: [-15, -8.5]
+  # dictionary containing lens mode configurations
+  lens_mode_config:
+    "6kV_kmodem4.0_20VTOF_v3.sav":
+      Extr: 6000.0
+      UCA: 1200
+      UFA: 600.0
+      Z1: 2452.9
+      Z2: 1489.9
+      A: 420.07
+      B: 2494.8
+      C: 489.2
+      D: 228.05
+      E: 113.82
+      F: 54.232
+      G: 20.0
+      H: 25.5
+      I: 36.0
+      TOF: 20.0
+      MCPfront: 20.0
+    "6kV_kmodem4.0_30VTOF_453ns_focus.sav":
+      Extr: 6000.0
+      UCA: 1200
+      UFA: 600.0
+      Z1: 2452.9
+      Z2: 1489.9
+      A: 403.07
+      B: 2500
+      C: 422.25
+      D: 208.88
+      E: 199.49
+      F: 68.735
+      G: 30.0
+      H: 30.0
+      I: 44.5
+      TOF: 30.0
+      MCPfront: 30.0
+
+nexus:
+  # pynxtools reader to use for saving NXmpes files
+  reader: "mpes"
+  # NeXus application definition to use for saving
+  definition: "NXmpes"
+  # List conatining additional input files to be handed to the pynxtools converter tool,
+  # e.g. containing a configuration file, and additional metadata.
+  input_files: ["../sed/config/NXmpes_config.json"]
diff --git a/sed/core/processor.py b/sed/core/processor.py
index 0f848ba5bf2d2db53edddd37bd2c3102f801cdbf..4dc7a626f70cd74f1f2cfc48d928a93dc531cdc1 100644
--- a/sed/core/processor.py
+++ b/sed/core/processor.py
@@ -87,9 +87,14 @@ class SedProcessor:
                 defined in the config. Defaults to None.
             collect_metadata (bool): Option to collect metadata from files.
                 Defaults to False.
-            **kwds: Keyword arguments passed to the reader.
+            **kwds: Keyword arguments passed to parse_config and to the reader.
         """
-        self._config = parse_config(config, **kwds)
+        config_kwds = {
+            key: value for key, value in kwds.items() if key in parse_config.__code__.co_varnames
+        }
+        for key in config_kwds.keys():
+            del kwds[key]
+        self._config = parse_config(config, **config_kwds)
         num_cores = self._config.get("binning", {}).get("num_cores", N_CPU - 1)
         if num_cores >= N_CPU:
             num_cores = N_CPU - 1
@@ -106,9 +111,6 @@ class SedProcessor:
         self._binned: xr.DataArray = None
         self._pre_binned: xr.DataArray = None
 
-        self._dimensions: List[str] = []
-        self._coordinates: Dict[Any, Any] = {}
-        self.axis: Dict[Any, Any] = {}
         self._attributes = MetaHandler(meta=metadata)
 
         loader_name = self._config["core"]["loader"]
@@ -161,84 +163,76 @@ class SedProcessor:
             df_str = "Data Frame: No Data loaded"
         else:
             df_str = self._dataframe.__repr__()
-        coordinates_str = f"Coordinates: {self._coordinates}"
-        dimensions_str = f"Dimensions: {self._dimensions}"
-        pretty_str = df_str + "\n" + coordinates_str + "\n" + dimensions_str
+        attributes_str = f"Metadata: {self._attributes.metadata}"
+        pretty_str = df_str + "\n" + attributes_str
         return pretty_str
 
-    def __getitem__(self, val: str) -> pd.DataFrame:
-        """Accessor to the underlying data structure.
-
-        Args:
-            val (str): Name of the dataframe column to retrieve.
-
-        Returns:
-            pd.DataFrame: Selected dataframe column.
-        """
-        return self._dataframe[val]
-
     @property
-    def config(self) -> Dict[Any, Any]:
-        """Getter attribute for the config dictionary
+    def dataframe(self) -> Union[pd.DataFrame, ddf.DataFrame]:
+        """Accessor to the underlying dataframe.
 
         Returns:
-            Dict: The config dictionary.
+            Union[pd.DataFrame, ddf.DataFrame]: Dataframe object.
         """
-        return self._config
+        return self._dataframe
 
-    @config.setter
-    def config(self, config: Union[dict, str]):
-        """Setter function for the config dictionary.
+    @dataframe.setter
+    def dataframe(self, dataframe: Union[pd.DataFrame, ddf.DataFrame]):
+        """Setter for the underlying dataframe.
 
         Args:
-            config (Union[dict, str]): Config dictionary or path of config file
-                to load.
+            dataframe (Union[pd.DataFrame, ddf.DataFrame]): The dataframe object to set.
         """
-        self._config = parse_config(config)
-        num_cores = self._config.get("binning", {}).get("num_cores", N_CPU - 1)
-        if num_cores >= N_CPU:
-            num_cores = N_CPU - 1
-        self._config["binning"]["num_cores"] = num_cores
+        if not isinstance(dataframe, (pd.DataFrame, ddf.DataFrame)) or not isinstance(
+            dataframe,
+            self._dataframe.__class__,
+        ):
+            raise ValueError(
+                "'dataframe' has to be a Pandas or Dask dataframe and has to be of the same kind "
+                "as the dataframe loaded into the SedProcessor!.\n"
+                f"Loaded type: {self._dataframe.__class__}, provided type: {dataframe}.",
+            )
+        self._dataframe = dataframe
 
     @property
-    def dimensions(self) -> list:
-        """Getter attribute for the dimensions.
+    def attributes(self) -> dict:
+        """Accessor to the metadata dict.
 
         Returns:
-            list: List of dimensions.
+            dict: The metadata dict.
         """
-        return self._dimensions
+        return self._attributes.metadata
 
-    @dimensions.setter
-    def dimensions(self, dims: list):
-        """Setter function for the dimensions.
+    def add_attribute(self, attributes: dict, name: str, **kwds):
+        """Function to add element to the attributes dict.
 
         Args:
-            dims (list): List of dimensions to set.
+            attributes (dict): The attributes dictionary object to add.
+            name (str): Key under which to add the dictionary to the attributes.
         """
-        assert isinstance(dims, list)
-        self._dimensions = dims
+        self._attributes.add(
+            entry=attributes,
+            name=name,
+            **kwds,
+        )
 
     @property
-    def coordinates(self) -> dict:
-        """Getter attribute for the coordinates dict.
+    def config(self) -> Dict[Any, Any]:
+        """Getter attribute for the config dictionary
 
         Returns:
-            dict: Dictionary of coordinates.
+            Dict: The config dictionary.
         """
-        return self._coordinates
+        return self._config
 
-    @coordinates.setter
-    def coordinates(self, coords: dict):
-        """Setter function for the coordinates dict
+    @property
+    def files(self) -> List[str]:
+        """Getter attribute for the list of files
 
-        Args:
-            coords (dict): Dictionary of coordinates.
+        Returns:
+            List[str]: The list of loaded files
         """
-        assert isinstance(coords, dict)
-        self._coordinates = {}
-        for k, v in coords.items():
-            self._coordinates[k] = xr.DataArray(v)
+        return self._files
 
     def cpy(self, path: Union[str, List[str]]) -> Union[str, List[str]]:
         """Function to mirror a list of files or a folder from a network drive to a
@@ -419,17 +413,11 @@ class SedProcessor:
                 MomentumCorrector.feature_select()
         """
         if auto_detect:  # automatic feature selection
-            sigma = kwds.pop(
-                "sigma",
-                self._config.get("momentum", {}).get("sigma", 5),
-            )
-            fwhm = kwds.pop(
-                "fwhm",
-                self._config.get("momentum", {}).get("fwhm", 8),
-            )
+            sigma = kwds.pop("sigma", self._config["momentum"]["sigma"])
+            fwhm = kwds.pop("fwhm", self._config["momentum"]["fwhm"])
             sigma_radius = kwds.pop(
                 "sigma_radius",
-                self._config.get("momentum", {}).get("sigma_radius", 1),
+                self._config["momentum"]["sigma_radius"],
             )
             self.mc.feature_extract(
                 sigma=sigma,
@@ -452,18 +440,18 @@ class SedProcessor:
     # If no features have been selected before, use class defaults.
     def generate_splinewarp(
         self,
-        include_center: bool = True,
+        use_center: bool = None,
         **kwds,
     ):
         """3. Step of the distortion correction workflow: Generate the correction
         function restoring the symmetry in the image using a splinewarp algortihm.
 
         Args:
-            include_center (bool, optional): Option to include the position of the
-                center point in the correction. Defaults to True.
+            use_center (bool, optional): Option to use the position of the
+                center point in the correction. Default is read from config, or set to True.
             **kwds: Keyword arguments for MomentumCorrector.spline_warp_estimate().
         """
-        self.mc.spline_warp_estimate(include_center=include_center, **kwds)
+        self.mc.spline_warp_estimate(use_center=use_center, **kwds)
 
         if self.mc.slice is not None:
             print("Original slice with reference features")
@@ -506,7 +494,7 @@ class SedProcessor:
         try:
             for point in self.mc.pouter_ord:
                 points.append([float(i) for i in point])
-            if self.mc.pcent:
+            if self.mc.include_center:
                 points.append([float(i) for i in self.mc.pcent])
         except AttributeError as exc:
             raise AttributeError(
@@ -514,7 +502,12 @@ class SedProcessor:
             ) from exc
         config = {
             "momentum": {
-                "correction": {"rotation_symmetry": self.mc.rotsym, "feature_points": points},
+                "correction": {
+                    "rotation_symmetry": self.mc.rotsym,
+                    "feature_points": points,
+                    "include_center": self.mc.include_center,
+                    "use_center": self.mc.use_center,
+                },
             },
         }
         save_config(config, filename, overwrite)
@@ -529,6 +522,7 @@ class SedProcessor:
         angle: float = 0,
         apply: bool = False,
         use_correction: bool = True,
+        reset: bool = True,
     ):
         """3. step of the distortion correction workflow: Generate an interactive panel
         to adjust affine transformations that are applied to the image. Applies first
@@ -548,6 +542,8 @@ class SedProcessor:
                 transformations. Defaults to False.
             use_correction (bool, option): Whether to use the spline warp correction
                 or not. Defaults to True.
+            reset (bool, optional):
+                Option to reset the correction before transformation. Defaults to True.
         """
         # Generate homomorphy as default if no distortion correction has been applied
         if self.mc.slice_corrected is None:
@@ -557,20 +553,21 @@ class SedProcessor:
                 )
             self.mc.slice_corrected = self.mc.slice
 
+        if not use_correction:
+            self.mc.reset_deformation()
+
         if self.mc.cdeform_field is None or self.mc.rdeform_field is None:
-            # Generate default distortion correction
+            # Generate distortion correction from config values
             self.mc.add_features()
             self.mc.spline_warp_estimate()
 
-        if not use_correction:
-            self.mc.reset_deformation()
-
         self.mc.pose_adjustment(
             scale=scale,
             xtrans=xtrans,
             ytrans=ytrans,
             angle=angle,
             apply=apply,
+            reset=reset,
         )
 
     # 5. Apply the momentum correction to the dataframe
@@ -647,10 +644,7 @@ class SedProcessor:
                 in the class. Defaults to False.
         """
         if point_b is None:
-            point_b = self._config.get("momentum", {}).get(
-                "center_pixel",
-                [256, 256],
-            )
+            point_b = self._config["momentum"]["center_pixel"]
 
         self.mc.select_k_range(
             point_a=point_a,
@@ -797,13 +791,13 @@ class SedProcessor:
             filename = "sed_config.yaml"
         correction = {}
         try:
-            for key in self.ec.correction.keys():
+            for key, val in self.ec.correction.items():
                 if key == "correction_type":
-                    correction[key] = self.ec.correction[key]
+                    correction[key] = val
                 elif key == "center":
-                    correction[key] = [float(i) for i in self.ec.correction[key]]
+                    correction[key] = [float(i) for i in val]
                 else:
-                    correction[key] = float(self.ec.correction[key])
+                    correction[key] = float(val)
         except AttributeError as exc:
             raise AttributeError(
                 "Energy correction parameters not found, need to generate parameters first!",
@@ -897,15 +891,12 @@ class SedProcessor:
             bias_key=bias_key,
         )
         if (normalize is not None and normalize is True) or (
-            normalize is None and self._config.get("energy", {}).get("normalize", True)
+            normalize is None and self._config["energy"]["normalize"]
         ):
             if span is None:
-                span = self._config.get("energy", {}).get("normalize_span", 7)
+                span = self._config["energy"]["normalize_span"]
             if order is None:
-                order = self._config.get("energy", {}).get(
-                    "normalize_order",
-                    1,
-                )
+                order = self._config["energy"]["normalize_order"]
             self.ec.normalize(smooth=True, span=span, order=order)
         self.ec.view(
             traces=self.ec.traces_normed,
@@ -922,6 +913,7 @@ class SedProcessor:
         mode: str = "replace",
         radius: int = None,
         peak_window: int = None,
+        apply: bool = False,
     ):
         """2. step of the energy calibration workflow: Find a peak within a given range
         for the indicated reference trace, and tries to find the same peak for all
@@ -944,35 +936,46 @@ class SedProcessor:
             peak_window (int, optional): Peak_window parameter for the peak detection
                 algorthm. amount of points that have to have to behave monotoneously
                 around a peak. Defaults to config["energy"]["peak_window"].
+            apply (bool, optional): Option to directly apply the provided parameters.
+                Defaults to False.
         """
         if radius is None:
-            radius = self._config.get("energy", {}).get("fastdtw_radius", 2)
-        self.ec.add_features(
-            ranges=ranges,
-            ref_id=ref_id,
-            infer_others=infer_others,
-            mode=mode,
-            radius=radius,
-        )
-        self.ec.view(
-            traces=self.ec.traces_normed,
-            segs=self.ec.featranges,
-            xaxis=self.ec.tof,
-            backend="bokeh",
-        )
-        print(self.ec.featranges)
+            radius = self._config["energy"]["fastdtw_radius"]
         if peak_window is None:
-            peak_window = self._config.get("energy", {}).get("peak_window", 7)
-        try:
-            self.ec.feature_extract(peak_window=peak_window)
-            self.ec.view(
+            peak_window = self._config["energy"]["peak_window"]
+        if not infer_others:
+            self.ec.add_ranges(
+                ranges=ranges,
+                ref_id=ref_id,
+                infer_others=infer_others,
+                mode=mode,
+                radius=radius,
+            )
+            print(self.ec.featranges)
+            try:
+                self.ec.feature_extract(peak_window=peak_window)
+                self.ec.view(
+                    traces=self.ec.traces_normed,
+                    segs=self.ec.featranges,
+                    xaxis=self.ec.tof,
+                    peaks=self.ec.peaks,
+                    backend="bokeh",
+                )
+            except IndexError:
+                print("Could not determine all peaks!")
+                raise
+        else:
+            # New adjustment tool
+            assert isinstance(ranges, tuple)
+            self.ec.adjust_ranges(
+                ranges=ranges,
+                ref_id=ref_id,
                 traces=self.ec.traces_normed,
-                peaks=self.ec.peaks,
-                backend="bokeh",
+                infer_others=infer_others,
+                radius=radius,
+                peak_window=peak_window,
+                apply=apply,
             )
-        except IndexError:
-            print("Could not determine all peaks!")
-            raise
 
     # 3. Fit the energy calibration relation
     def calibrate_energy_axis(
@@ -1007,16 +1010,10 @@ class SedProcessor:
                 Defaults to config["energy"]["energy_scale"]
         """
         if method is None:
-            method = self._config.get("energy", {}).get(
-                "calibration_method",
-                "lmfit",
-            )
+            method = self._config["energy"]["calibration_method"]
 
         if energy_scale is None:
-            energy_scale = self._config.get("energy", {}).get(
-                "energy_scale",
-                "kinetic",
-            )
+            energy_scale = self._config["energy"]["energy_scale"]
 
         self.ec.calibrate(
             ref_id=ref_id,
@@ -1082,10 +1079,12 @@ class SedProcessor:
         calibration = {}
         try:
             for (key, value) in self.ec.calibration.items():
-                if key in ["axis", "refid"]:
+                if key in ["axis", "refid", "Tmat", "bvec"]:
                     continue
                 if key == "energy_scale":
                     calibration[key] = value
+                elif key == "coeffs":
+                    calibration[key] = [float(i) for i in value]
                 else:
                     calibration[key] = float(value)
         except AttributeError as exc:
@@ -1199,7 +1198,7 @@ class SedProcessor:
                 Defaults to config["dataframe"]["jitter_cols"].
         """
         if cols is None:
-            cols = self._config.get("dataframe", {}).get(
+            cols = self._config["dataframe"].get(
                 "jitter_cols",
                 self._dataframe.columns,
             )  # jitter all columns
@@ -1239,23 +1238,17 @@ class SedProcessor:
             xr.DataArray: pre-binned data-array.
         """
         if axes is None:
-            axes = self._config.get("momentum", {}).get(
-                "axes",
-                ["@x_column, @y_column, @tof_column"],
-            )
+            axes = self._config["momentum"]["axes"]
         for loc, axis in enumerate(axes):
             if axis.startswith("@"):
-                axes[loc] = self._config.get("dataframe").get(axis.strip("@"))
+                axes[loc] = self._config["dataframe"].get(axis.strip("@"))
 
         if bins is None:
-            bins = self._config.get("momentum", {}).get(
-                "bins",
-                [512, 512, 300],
-            )
+            bins = self._config["momentum"]["bins"]
         if ranges is None:
-            ranges_ = self._config.get("momentum", {}).get(
-                "ranges",
-                [[-256, 1792], [-256, 1792], [128000, 138000]],
+            ranges_ = list(self._config["momentum"]["ranges"])
+            ranges_[2] = np.asarray(ranges_[2]) / 2 ** (
+                self._config["dataframe"]["tof_binning"] - 1
             )
             ranges = [cast(Tuple[float, float], tuple(v)) for v in ranges_]
 
@@ -1423,8 +1416,21 @@ class SedProcessor:
             bins = self._config["histogram"]["bins"]
         if axes is None:
             axes = self._config["histogram"]["axes"]
+        axes = list(axes)
+        for loc, axis in enumerate(axes):
+            if axis.startswith("@"):
+                axes[loc] = self._config["dataframe"].get(axis.strip("@"))
         if ranges is None:
-            ranges = self._config["histogram"]["ranges"]
+            ranges = list(self._config["histogram"]["ranges"])
+            for loc, axis in enumerate(axes):
+                if axis == self._config["dataframe"]["tof_column"]:
+                    ranges[loc] = np.asarray(ranges[loc]) / 2 ** (
+                        self._config["dataframe"]["tof_binning"] - 1
+                    )
+                elif axis == self._config["dataframe"]["adc_column"]:
+                    ranges[loc] = np.asarray(ranges[loc]) / 2 ** (
+                        self._config["dataframe"]["adc_binning"] - 1
+                    )
 
         input_types = map(type, [axes, bins, ranges])
         allowed_types = [list, tuple]
@@ -1491,6 +1497,8 @@ class SedProcessor:
                   config["nexus"]["definition"]
                 - **input_files**: A list of input files to pass to the reader.
                   Defaults to config["nexus"]["input_files"]
+                - **eln_data**: An electronic-lab-notebook file in '.yaml' format
+                  to add to the list of files to pass to the reader.
         """
         if self._binned is None:
             raise NameError("Need to bin data first!")
@@ -1510,18 +1518,27 @@ class SedProcessor:
                 **kwds,
             )
         elif extension in (".nxs", ".nexus"):
-            reader = kwds.pop("reader", self._config["nexus"]["reader"])
-            definition = kwds.pop(
-                "definition",
-                self._config["nexus"]["definition"],
-            )
-            input_files = kwds.pop(
-                "input_files",
-                self._config["nexus"]["input_files"],
-            )
+            try:
+                reader = kwds.pop("reader", self._config["nexus"]["reader"])
+                definition = kwds.pop(
+                    "definition",
+                    self._config["nexus"]["definition"],
+                )
+                input_files = kwds.pop(
+                    "input_files",
+                    self._config["nexus"]["input_files"],
+                )
+            except KeyError as exc:
+                raise ValueError(
+                    "The nexus reader, definition and input files need to be provide!",
+                ) from exc
+
             if isinstance(input_files, str):
                 input_files = [input_files]
 
+            if "eln_data" in kwds:
+                input_files.append(kwds.pop("eln_data"))
+
             to_nexus(
                 data=self._binned,
                 faddr=faddr,
@@ -1536,31 +1553,6 @@ class SedProcessor:
                 f"Unrecognized file format: {extension}.",
             )
 
-    def add_dimension(self, name: str, axis_range: Tuple):
-        """Add a dimension axis.
-
-        Args:
-            name (str): name of the axis
-            axis_range (Tuple): range for the axis.
-
-        Raises:
-            ValueError: Raised if an axis with that name already exists.
-        """
-        if name in self._coordinates:
-            raise ValueError(f"Axis {name} already exists")
-
-        self.axis[name] = self.make_axis(axis_range)
-
-    def make_axis(self, axis_range: Tuple) -> np.ndarray:
-        """Function to make an axis.
-
-        Args:
-            axis_range (Tuple): range for the new axis.
-        """
-
-        # TODO: What shall this function do?
-        return np.arange(*axis_range)
-
     def pre_process(self) -> ddf.DataFrame:
         """Apply preprocessing pipeline to dataframe"""
         if len(self._preprocessing_pipeline) == 0:
diff --git a/sed/loader/flash/loader.py b/sed/loader/flash/loader.py
index 8d4f5cf292bebc3e062a57e107a9518143b54185..f5f5c803d173f6615404b1277827087efd0d5548 100644
--- a/sed/loader/flash/loader.py
+++ b/sed/loader/flash/loader.py
@@ -1,9 +1,12 @@
 """
 This module implements the flash data loader.
-The raw hdf5 data is saved into parquet files and loaded as a dask dataframe.
-If there are multiple files, the NaNs are forward filled.
+This loader currently supports hextof, wespe and instruments with similar structure.
+The raw hdf5 data is combined and saved into buffer files and loaded as a dask dataframe.
+The dataframe is a amalgamation of all h5 files for a combination of runs, where the NaNs are
+automatically forward filled across different files.
+This can then be saved as a parquet for out-of-sed processing and reread back to access other
+sed funtionality.
 """
-import os
 from functools import reduce
 from itertools import compress
 from pathlib import Path
@@ -29,9 +32,9 @@ from sed.loader.utils import parse_h5_keys
 
 class FlashLoader(BaseLoader):
     """
-    The class generates multiindexed multidimensional pandas dataframes
-    from the new FLASH dataformat resolved by both macro and microbunches
-    alongside electrons.
+    The class generates multiindexed multidimensional pandas dataframes from the new FLASH
+    dataformat resolved by both macro and microbunches alongside electrons.
+    Only the read_dataframe (inherited and implemented) method is accessed by other modules.
     """
 
     __name__ = "flash"
@@ -41,11 +44,129 @@ class FlashLoader(BaseLoader):
     def __init__(self, config: dict) -> None:
 
         super().__init__(config=config)
+        self.multi_index = ["trainId", "pulseId", "electronId"]
         self.index_per_electron: MultiIndex = None
         self.index_per_pulse: MultiIndex = None
-        self.parquet_names: List[Path] = []
         self.failed_files_error: List[str] = []
 
+    def initialize_paths(self) -> Tuple[List[Path], Path]:
+        """
+        Initializes the paths based on the configuration.
+
+        Returns:
+            Tuple[List[Path], Path]: A tuple containing a list of raw data directories
+            paths and the parquet data directory path.
+
+        Raises:
+            ValueError: If required values are missing from the configuration.
+            FileNotFoundError: If the raw data directories are not found.
+        """
+        # Parses to locate the raw beamtime directory from config file
+        if "paths" in self._config["core"]:
+            data_raw_dir = [
+                Path(self._config["core"]["paths"].get("data_raw_dir", "")),
+            ]
+            data_parquet_dir = Path(
+                self._config["core"]["paths"].get("data_parquet_dir", ""),
+            )
+
+        else:
+            try:
+                beamtime_id = self._config["core"]["beamtime_id"]
+                year = self._config["core"]["year"]
+                daq = self._config["dataframe"]["daq"]
+            except KeyError as exc:
+                raise ValueError(
+                    "The beamtime_id, year and daq are required.",
+                ) from exc
+
+            beamtime_dir = Path(
+                self._config["dataframe"]["beamtime_dir"][self._config["core"]["beamline"]],
+            )
+            beamtime_dir = beamtime_dir.joinpath(f"{year}/data/{beamtime_id}/")
+
+            # Use pathlib walk to reach the raw data directory
+            data_raw_dir = []
+            raw_path = beamtime_dir.joinpath("raw")
+
+            for path in raw_path.glob("**/*"):
+                if path.is_dir():
+                    dir_name = path.name
+                    if dir_name.startswith("express-") or dir_name.startswith(
+                        "online-",
+                    ):
+                        data_raw_dir.append(path.joinpath(daq))
+                    elif dir_name == daq.upper():
+                        data_raw_dir.append(path)
+
+            if not data_raw_dir:
+                raise FileNotFoundError("Raw data directories not found.")
+
+            parquet_path = "processed/parquet"
+            data_parquet_dir = beamtime_dir.joinpath(parquet_path)
+
+        data_parquet_dir.mkdir(parents=True, exist_ok=True)
+
+        return data_raw_dir, data_parquet_dir
+
+    def get_files_from_run_id(
+        self,
+        run_id: str,
+        folders: Union[str, Sequence[str]] = None,
+        extension: str = "h5",
+        **kwds,
+    ) -> List[str]:
+        """Returns a list of filenames for a given run located in the specified directory
+        for the specified data acquisition (daq).
+
+        Args:
+            run_id (str): The run identifier to locate.
+            folders (Union[str, Sequence[str]], optional): The directory(ies) where the raw
+                data is located. Defaults to config["core"]["base_folder"].
+            extension (str, optional): The file extension. Defaults to "h5".
+            kwds: Keyword arguments:
+                - daq (str): The data acquisition identifier.
+                Defaults to config["dataframe"]["daq"].
+
+        Returns:
+            List[str]: A list of path strings representing the collected file names.
+
+        Raises:
+            FileNotFoundError: If no files are found for the given run in the directory.
+        """
+        # Define the stream name prefixes based on the data acquisition identifier
+        stream_name_prefixes = self._config["dataframe"]["stream_name_prefixes"]
+
+        if folders is None:
+            folders = self._config["core"]["base_folder"]
+
+        if isinstance(folders, str):
+            folders = [folders]
+
+        daq = kwds.pop("daq", self._config.get("dataframe", {}).get("daq"))
+
+        # Generate the file patterns to search for in the directory
+        file_pattern = f"{stream_name_prefixes[daq]}_run{run_id}_*." + extension
+
+        files: List[Path] = []
+        # Use pathlib to search for matching files in each directory
+        for folder in folders:
+            files.extend(
+                natsorted(
+                    Path(folder).glob(file_pattern),
+                    key=lambda filename: str(filename).rsplit("_", maxsplit=1)[-1],
+                ),
+            )
+
+        # Check if any files are found
+        if not files:
+            raise FileNotFoundError(
+                f"No files found for run {run_id} in directory {str(folders)}",
+            )
+
+        # Return the list of found files
+        return [str(file.resolve()) for file in files]
+
     @property
     def available_channels(self) -> List:
         """Returns the channel names that are available for use,
@@ -126,8 +247,7 @@ class FlashLoader(BaseLoader):
             names=["trainId", "pulseId"],
         )
 
-        # Calculate the electron counts per pulseId
-        # unique preserves the order of appearance
+        # Calculate the electron counts per pulseId unique preserves the order of appearance
         electron_counts = index_temp.value_counts()[index_temp.unique()].values
 
         # Series object for indexing with electrons
@@ -142,7 +262,7 @@ class FlashLoader(BaseLoader):
         # Create a pandas MultiIndex using the exploded datasets
         self.index_per_electron = MultiIndex.from_arrays(
             (microbunches.index, microbunches.values, electrons),
-            names=["trainId", "pulseId", "electronId"],
+            names=self.multi_index,
         )
 
     def create_multi_index_per_pulse(
@@ -151,16 +271,15 @@ class FlashLoader(BaseLoader):
         np_array: np.ndarray,
     ) -> None:
         """
-        Creates an index per pulse using a pulse resolved channel's macrobunch ID,
-        for usage with the pulse resolved pandas DataFrame.
+        Creates an index per pulse using a pulse resolved channel's macrobunch ID, for usage with
+        the pulse resolved pandas DataFrame.
 
         Args:
             train_id (Series): The train ID Series.
             np_array (np.ndarray): The numpy array containing the pulse resolved data.
 
         Notes:
-            - This method creates a MultiIndex with trainId and pulseId as the
-                index levels.
+            - This method creates a MultiIndex with trainId and pulseId as the index levels.
         """
 
         # Create a pandas MultiIndex, useful for comparing electron and
@@ -183,8 +302,8 @@ class FlashLoader(BaseLoader):
             channel (str): The name of the channel.
 
         Returns:
-            Tuple[Series, np.ndarray]: A tuple containing the train ID Series
-            and the numpy array for the channel's data.
+            Tuple[Series, np.ndarray]: A tuple containing the train ID Series and the numpy array
+            for the channel's data.
 
         """
         # Get the data from the necessary h5 file and channel
@@ -228,9 +347,8 @@ class FlashLoader(BaseLoader):
             DataFrame: The pandas DataFrame for the channel's data.
 
         Notes:
-            The microbunch resolved data is exploded and converted to a DataFrame.
-            The MultiIndex is set, and the NaN values are dropped, alongside the
-            pulseId = 0 (meaningless).
+            The microbunch resolved data is exploded and converted to a DataFrame. The MultiIndex
+            is set, and the NaN values are dropped, alongside the pulseId = 0 (meaningless).
 
         """
         return (
@@ -266,18 +384,17 @@ class FlashLoader(BaseLoader):
             DataFrame: The pandas DataFrame for the channel's data.
 
         Notes:
-            - For auxillary channels, the macrobunch resolved data is repeated 499
-              times to be compared to electron resolved data for each auxillary
-              channel. The data is then converted to a multicolumn DataFrame.
-            - For all other pulse resolved channels, the macrobunch resolved
-              data is exploded to a DataFrame and the MultiIndex is set.
+            - For auxillary channels, the macrobunch resolved data is repeated 499 times to be
+              compared to electron resolved data for each auxillary channel. The data is then
+              converted to a multicolumn DataFrame.
+            - For all other pulse resolved channels, the macrobunch resolved data is exploded
+              to a DataFrame and the MultiIndex is set.
 
         """
 
         # Special case for auxillary channels
         if channel == "dldAux":
-            # Checks the channel dictionary for correct slices and creates a
-            # multicolumn DataFrame
+            # Checks the channel dictionary for correct slices and creates a multicolumn DataFrame
             data_frames = (
                 Series(
                     (np_array[i, value] for i in train_id.index),
@@ -292,8 +409,7 @@ class FlashLoader(BaseLoader):
 
         # For all other pulse resolved channels
         else:
-            # Macrobunch resolved data is exploded to a DataFrame and
-            # the MultiIndex is set
+            # Macrobunch resolved data is exploded to a DataFrame and the MultiIndex is set
 
             # Creates the index_per_pulse for the given channel
             self.create_multi_index_per_pulse(train_id, np_array)
@@ -337,18 +453,16 @@ class FlashLoader(BaseLoader):
         """
         Returns a pandas DataFrame for a given channel name from a given file.
 
-        This method takes an h5py.File object `h5_file` and a channel name `channel`,
-        and returns a pandas DataFrame containing the data for that channel from the
-        file. The format of the DataFrame depends on the channel's format specified
-        in the configuration.
+        This method takes an h5py.File object `h5_file` and a channel name `channel`, and returns
+        a pandas DataFrame containing the data for that channel from the file. The format of the
+        DataFrame depends on the channel's format specified in the configuration.
 
         Args:
             h5_file (h5py.File): The h5py.File object representing the HDF5 file.
             channel (str): The name of the channel.
 
         Returns:
-            Union[Series, DataFrame]: A pandas Series or DataFrame representing the
-            channel's data.
+            Union[Series, DataFrame]: A pandas Series or DataFrame representing the channel's data.
 
         Raises:
             ValueError: If the channel has an undefined format.
@@ -415,9 +529,9 @@ class FlashLoader(BaseLoader):
         """
         Concatenates the channels from the provided h5py.File into a pandas DataFrame.
 
-        This method takes an h5py.File object `h5_file` and concatenates the channels
-        present in the file into a single pandas DataFrame. The concatenation is
-        performed based on the available channels specified in the configuration.
+        This method takes an h5py.File object `h5_file` and concatenates the channels present in
+        the file into a single pandas DataFrame. The concatenation is performed based on the
+        available channels specified in the configuration.
 
         Args:
             h5_file (h5py.File): The h5py.File object representing the HDF5 file.
@@ -461,9 +575,9 @@ class FlashLoader(BaseLoader):
         """
         Create pandas DataFrames for the given file.
 
-        This method loads an HDF5 file specified by `file_path` and constructs a pandas
-        DataFrame from the datasets within the file. The order of datasets in the
-        DataFrames is the opposite of the order specified by channel names.
+        This method loads an HDF5 file specified by `file_path` and constructs a pandas DataFrame
+        from the datasets within the file. The order of datasets in the DataFrames is the opposite
+        of the order specified by channel names.
 
         Args:
             file_path (Path): Path to the input HDF5 file.
@@ -477,13 +591,12 @@ class FlashLoader(BaseLoader):
             self.reset_multi_index()  # Reset MultiIndexes for next file
             return self.concatenate_channels(h5_file)
 
-    def h5_to_parquet(self, h5_path: Path, parquet_path: Path) -> None:
+    def create_buffer_file(self, h5_path: Path, parquet_path: Path) -> None:
         """
-        Convert HDF5 file to Parquet format.
+        Converts an HDF5 file to Parquet format to create a buffer file.
 
-        This method uses the `create_dataframe_per_file` method to create dataframes
-        from individual files within an HDF5 file. The resulting dataframe is then
-        saved to a Parquet file.
+        This method uses `create_dataframe_per_file` method to create dataframes from individual
+        files within an HDF5 file. The resulting dataframe is then saved to a Parquet file.
 
         Args:
             h5_path (Path): Path to the input HDF5 file.
@@ -496,14 +609,72 @@ class FlashLoader(BaseLoader):
         try:
             (
                 self.create_dataframe_per_file(h5_path)
-                .reset_index(level=["trainId", "pulseId", "electronId"])
+                .reset_index(level=self.multi_index)
                 .to_parquet(parquet_path, index=False)
             )
         except ValueError as failed_string_error:
             self.failed_files_error.append(
                 f"{parquet_path}: {failed_string_error}",
             )
-            self.parquet_names.remove(parquet_path)
+
+    def buffer_file_handler(self, data_parquet_dir: Path, detector: str):
+        """
+        Handles the conversion of buffer files (h5 to parquet) and returns the filenames.
+
+        Args:
+            data_parquet_dir (Path): Directory where the parquet files will be stored.
+            detector (str): Detector name.
+
+        Returns:
+            Tuple[List[Path], List[Path]]: Two lists, one for h5 file paths and one for
+            corresponding parquet file paths.
+
+        Raises:
+            FileNotFoundError: If the conversion fails for any files or no data is available.
+        """
+
+        # Create the directory for buffer parquet files
+        buffer_file_dir = data_parquet_dir.joinpath("buffer")
+        buffer_file_dir.mkdir(parents=True, exist_ok=True)
+
+        # Create two separate lists for h5 and parquet file paths
+        h5_filenames = [Path(file) for file in self.files]
+        parquet_filenames = [
+            buffer_file_dir.joinpath(Path(file).stem + detector) for file in self.files
+        ]
+
+        # Raise a value error if no data is available after the conversion
+        if len(h5_filenames) == 0:
+            raise ValueError("No data available. Probably failed reading all h5 files")
+
+        # Choose files to read
+        files_to_read = [
+            (h5_path, parquet_path)
+            for h5_path, parquet_path in zip(h5_filenames, parquet_filenames)
+            if not parquet_path.exists()
+        ]
+
+        print(f"Reading files: {len(files_to_read)} new files of {len(h5_filenames)} total.")
+
+        # Initialize the indices for create_buffer_file conversion
+        self.reset_multi_index()
+
+        # Convert the remaining h5 files to parquet in parallel if there are any
+        if len(files_to_read) > 0:
+            Parallel(n_jobs=len(files_to_read), verbose=10)(
+                delayed(self.create_buffer_file)(h5_path, parquet_path)
+                for h5_path, parquet_path in files_to_read
+            )
+
+        # Raise an error if the conversion failed for any files
+        if self.failed_files_error:
+            raise FileNotFoundError(
+                "Conversion failed for the following files:\n" + "\n".join(self.failed_files_error),
+            )
+
+        print("All files converted successfully!")
+
+        return h5_filenames, parquet_filenames
 
     def fill_na(
         self,
@@ -519,17 +690,15 @@ class FlashLoader(BaseLoader):
             dd.DataFrame: Concatenated dataframe with filled NaN values.
 
         Notes:
-            This method is specific to the flash data structure and is used to fill NaN
-            values in certain channels that only store information at a lower frequency
-            The low frequency channels are exploded to match the dimensions of higher
-            frequency channels, but they may contain NaNs in the other columns. This
-            method fills the NaNs for the specific channels (per_pulse and per_train).
+            This method is specific to the flash data structure and is used to fill NaN values in
+            certain channels that only store information at a lower frequency. The low frequency
+            channels are exploded to match the dimensions of higher frequency channels, but they
+            may contain NaNs in the other columns. This method fills the NaNs for the specific
+            channels (per_pulse and per_train).
 
         """
         # Channels to fill NaN values
-        channels: List[str] = self.get_channels_by_format(
-            ["per_pulse", "per_train"],
-        )
+        channels: List[str] = self.get_channels_by_format(["per_pulse", "per_train"])
 
         # Fill NaN values within each dataframe
         for i, _ in enumerate(dataframes):
@@ -561,6 +730,78 @@ class FlashLoader(BaseLoader):
         # Concatenate the filled dataframes
         return dd.concat(dataframes)
 
+    def parquet_handler(
+        self,
+        data_parquet_dir: Path,
+        detector: str = "",
+        parquet_path: Path = None,
+        converted: bool = False,
+        load_parquet: bool = False,
+        save_parquet: bool = False,
+    ):
+        """
+        Handles loading and saving of parquet files based on the provided parameters.
+
+        Args:
+            data_parquet_dir (Path): Directory where the parquet files are located.
+            detector (str, optional): Adds a identifier for parquets to distinguish multidetector
+                systems.
+            parquet_path (str, optional): Path to the combined parquet file.
+            converted (bool, optional): True if data is augmented by adding additional columns
+                externally and saved into converted folder.
+            load_parquet (bool, optional): Loads the entire parquet into the dd dataframe.
+            save_parquet (bool, optional): Saves the entire dataframe into a parquet.
+
+        Returns:
+            dataframe: Dataframe containing the loaded or processed data.
+
+        Raises:
+            FileNotFoundError: If the requested parquet file is not found.
+
+        """
+
+        # Construct the parquet path if not provided
+        if parquet_path is None:
+            parquet_name = "_".join(str(run) for run in self.runs)
+            parquet_dir = data_parquet_dir.joinpath("converted") if converted else data_parquet_dir
+
+            parquet_path = parquet_dir.joinpath(
+                "run_" + parquet_name + detector,
+            ).with_suffix(".parquet")
+
+        # Check if load_parquet is flagged and then load the file if it exists
+        if load_parquet:
+            try:
+                dataframe = dd.read_parquet(parquet_path)
+            except Exception as exc:
+                raise FileNotFoundError(
+                    "The final parquet for this run(s) does not exist yet. "
+                    "If it is in another location, please provide the path as parquet_path.",
+                ) from exc
+
+        else:
+            # Obtain the filenames from the method which handles buffer file creation/reading
+            _, parquet_filenames = self.buffer_file_handler(
+                data_parquet_dir,
+                detector,
+            )
+
+            # Read all parquet files using dask and concatenate into one dataframe after filling
+            dataframe = self.fill_na(
+                [dd.read_parquet(file) for file in parquet_filenames],
+            )
+
+            dataframe = dataframe.dropna(
+                subset=self.get_channels_by_format(["per_electron"]),
+            )
+
+        # Save the dataframe as parquet if requested
+        if save_parquet:
+            dataframe.compute().reset_index(drop=True).to_parquet(parquet_path)
+            print("Combined parquet file saved.")
+
+        return dataframe
+
     def parse_metadata(self) -> dict:
         """Uses the MetadataRetriever class to fetch metadata from scicat for each run.
 
@@ -600,38 +841,28 @@ class FlashLoader(BaseLoader):
         Read express data from the DAQ, generating a parquet in between.
 
         Args:
-            files (Union[str, Sequence[str]], optional): File path(s) to process.
+            files (Union[str, Sequence[str]], optional): File path(s) to process. Defaults to None.
+            folders (Union[str, Sequence[str]], optional): Path to folder(s) where files are stored
+                Path has priority such that if it's specified, the specified files will be ignored.
                 Defaults to None.
-            folders (Union[str, Sequence[str]], optional): Path to folder(s) where files
-                are stored. Path has priority such that if it's specified, the specified
-                files will be ignored. Defaults to None.
-            runs (Union[str, Sequence[str]], optional): Run identifier(s). Corresponding
-                files will be located in the location provided by ``folders``. Takes
-                precendence over ``files`` and ``folders``. Defaults to None.
+            runs (Union[str, Sequence[str]], optional): Run identifier(s). Corresponding files will
+                be located in the location provided by ``folders``. Takes precendence over
+                ``files`` and ``folders``. Defaults to None.
             ftype (str, optional): The file extension type. Defaults to "h5".
             metadata (dict, optional): Additional metadata. Defaults to None.
-            collect_metadata (bool, optional): Whether to collect metadata.
-                Defaults to False.
-
+            collect_metadata (bool, optional): Whether to collect metadata. Defaults to False.
 
         Returns:
-            Tuple[dd.DataFrame, dict]: A tuple containing the concatenated DataFrame
-            and metadata.
+            Tuple[dd.DataFrame, dict]: A tuple containing the concatenated DataFrame and metadata.
 
         Raises:
             ValueError: If neither 'runs' nor 'files'/'data_raw_dir' is provided.
-            FileNotFoundError: If the conversion fails for some files or no
-            data is available.
+            FileNotFoundError: If the conversion fails for some files or no data is available.
         """
 
         data_raw_dir, data_parquet_dir = self.initialize_paths()
 
-        # Create a per_file directory
-        temp_parquet_dir = data_parquet_dir.joinpath("per_file")
-        os.makedirs(temp_parquet_dir, exist_ok=True)
-
         # Prepare a list of names for the runs to read and parquets to write
-
         if runs is not None:
             files = []
             if isinstance(runs, (str, int)):
@@ -648,8 +879,8 @@ class FlashLoader(BaseLoader):
             super().read_dataframe(files=files, ftype=ftype)
 
         else:
-            # This call takes care of files and folders. As we have converted runs
-            # into files already, they are just stored in the class by this call.
+            # This call takes care of files and folders. As we have converted runs into files
+            # already, they are just stored in the class by this call.
             super().read_dataframe(
                 files=files,
                 folders=folders,
@@ -657,181 +888,11 @@ class FlashLoader(BaseLoader):
                 metadata=metadata,
             )
 
-        parquet_name = f"{temp_parquet_dir}/"
-        self.parquet_names = [Path(parquet_name + Path(file).stem) for file in self.files]
-        missing_files: List[Path] = []
-        missing_parquet_names: List[Path] = []
-
-        # Only read and write files which were not read already
-        for i, parquet_file in enumerate(self.parquet_names):
-            if not parquet_file.exists():
-                missing_files.append(Path(self.files[i]))
-                missing_parquet_names.append(parquet_file)
-
-        print(
-            f"Reading files: {len(missing_files)} new files of {len(self.files)} total.",
-        )
-
-        self.reset_multi_index()  # Initializes the indices for h5_to_parquet
-
-        # Run self.h5_to_parquet in parallel
-        if len(missing_files) > 0:
-            Parallel(n_jobs=len(missing_files), verbose=10)(
-                delayed(self.h5_to_parquet)(h5_path, parquet_path)
-                for h5_path, parquet_path in zip(
-                    missing_files,
-                    missing_parquet_names,
-                )
-            )
-
-        if self.failed_files_error:
-            raise FileNotFoundError(
-                "Conversion failed for the following files: \n"
-                + "\n".join(self.failed_files_error),
-            )
-
-        print("All files converted successfully!")
-
-        if len(self.parquet_names) == 0:
-            raise ValueError(
-                "No data available. Probably failed reading all h5 files",
-            )
-
-        print(
-            f"Loading {len(self.parquet_names)} dataframes. Failed reading "
-            f"{len(self.files)-len(self.parquet_names)} files.",
-        )
-        # Read all parquet files using dask and concatenate into one dataframe
-        # after filling
-        dataframe = self.fill_na(
-            [dd.read_parquet(parquet_file) for parquet_file in self.parquet_names],
-        )
-        dataframe = dataframe.dropna(
-            subset=self.get_channels_by_format(["per_electron"]),
-        )
+        dataframe = self.parquet_handler(data_parquet_dir, **kwds)
 
         metadata = self.parse_metadata() if collect_metadata else {}
 
         return dataframe, metadata
 
-    def get_files_from_run_id(
-        self,
-        run_id: str,
-        folders: Union[str, Sequence[str]] = None,
-        extension: str = "h5",
-        **kwds,
-    ) -> List[str]:
-        """Returns a list of filenames for a given run located in the specified directory
-        for the specified data acquisition (daq).
-
-        Args:
-            run_id (str): The run identifier to locate.
-            folders (Union[str, Sequence[str]], optional): The directory(ies) where the raw
-                data is located. Defaults to config["core"]["base_folder"].
-            extension (str, optional): The file extension. Defaults to "h5".
-            kwds: Keyword arguments:
-                - daq (str): The data acquisition identifier.
-                  Defaults to config["dataframe"]["daq"].
-
-        Returns:
-            List[str]: A list of path strings representing the collected file names.
-
-        Raises:
-            FileNotFoundError: If no files are found for the given run in the directory.
-        """
-        # Define the stream name prefixes based on the data acquisition identifier
-        stream_name_prefixes = self._config["dataframe"]["stream_name_prefixes"]
-
-        if folders is None:
-            folders = self._config["core"]["base_folder"]
-
-        if isinstance(folders, str):
-            folders = [folders]
-
-        daq = kwds.pop("daq", self._config.get("dataframe", {}).get("daq"))
-
-        # Generate the file patterns to search for in the directory
-        file_pattern = f"{stream_name_prefixes[daq]}_run{run_id}_*." + extension
-
-        files: List[Path] = []
-        # Use pathlib to search for matching files in each directory
-        for folder in folders:
-            files.extend(
-                natsorted(
-                    Path(folder).glob(file_pattern),
-                    key=lambda filename: str(filename).rsplit("_", maxsplit=1)[-1],
-                ),
-            )
-
-        # Check if any files are found
-        if not files:
-            raise FileNotFoundError(
-                f"No files found for run {run_id} in directory {str(folders)}",
-            )
-
-        # Return the list of found files
-        return [str(file.resolve()) for file in files]
-
-    def initialize_paths(self) -> Tuple[List[Path], Path]:
-        """
-        Initializes the paths based on the configuration.
-
-        Returns:
-            Tuple[List[Path], Path]: A tuple containing a list of raw data directories
-            paths and the parquet data directory path.
-
-        Raises:
-            ValueError: If required values are missing from the configuration.
-            FileNotFoundError: If the raw data directories are not found.
-        """
-        # Parses to locate the raw beamtime directory from config file
-        if "paths" in self._config["core"]:
-            data_raw_dir = [
-                Path(self._config["core"]["paths"].get("data_raw_dir", "")),
-            ]
-            data_parquet_dir = Path(
-                self._config["core"]["paths"].get("data_parquet_dir", ""),
-            )
-
-        else:
-            try:
-                beamtime_id = self._config["core"]["beamtime_id"]
-                year = self._config["core"]["year"]
-                daq = self._config["dataframe"]["daq"]
-            except KeyError as exc:
-                raise ValueError(
-                    "The beamtime_id, year and daq are required.",
-                ) from exc
-
-            beamtime_dir = Path(
-                self._config["dataframe"]["beamtime_dir"][self._config["core"]["beamline"]],
-            )
-            beamtime_dir = beamtime_dir.joinpath(f"{year}/data/{beamtime_id}/")
-
-            # Use os walk to reach the raw data directory
-            data_raw_dir = []
-            for root, dirs, files in os.walk(  # pylint: disable=W0612
-                beamtime_dir.joinpath("raw/"),
-            ):
-                for dir_name in dirs:
-                    if dir_name.startswith("express-") or dir_name.startswith(
-                        "online-",
-                    ):
-                        data_raw_dir.append(Path(root, dir_name, daq))
-                    elif dir_name == daq.upper():
-                        data_raw_dir.append(Path(root, dir_name))
-
-            if not data_raw_dir:
-                raise FileNotFoundError("Raw data directories not found.")
-
-            parquet_path = "processed/parquet"
-            data_parquet_dir = beamtime_dir.joinpath(parquet_path)
-
-        # TODO: This will fail of more than one level of directories needs to be created...
-        if not data_parquet_dir.exists():
-            os.mkdir(data_parquet_dir)
-
-        return data_raw_dir, data_parquet_dir
-
 
 LOADER = FlashLoader
diff --git a/sed/loader/flash/metadata.py b/sed/loader/flash/metadata.py
index 3324d6dbb2f0fd5e28477e99d3844047943a920a..de256e4d8b81120bbdb81ea40468011fbf76283b 100644
--- a/sed/loader/flash/metadata.py
+++ b/sed/loader/flash/metadata.py
@@ -2,6 +2,7 @@
 The module provides a MetadataRetriever class for retrieving metadata
 from a Scicatinstance based on beamtime and run IDs.
 """
+import warnings
 from typing import Dict
 from typing import Optional
 
@@ -79,16 +80,17 @@ class MetadataRetriever:
         Raises:
             Exception: If the request to retrieve metadata fails.
         """
-        # Create the dataset URL using the PID
-        dataset_response = requests.get(self._create_dataset_url_by_PID(pid), timeout=10)
-
-        # If the dataset request is successful, return the retrieved metadata
-        # as a JSON object
-        if dataset_response.ok:
+        try:
+            # Create the dataset URL using the PID
+            dataset_response = requests.get(self._create_dataset_url_by_PID(pid), timeout=10)
+            dataset_response.raise_for_status()  # Raise HTTPError if request fails
+            # If the dataset request is successful, return the retrieved metadata
+            # as a JSON object
             return dataset_response.json()
-
-        # If the request fails, raise an exception with the error message
-        raise ConnectionError(f"{dataset_response.text}")
+        except requests.exceptions.RequestException as exception:
+            # If the request fails, raise warning
+            warnings.warn(f"Failed to retrieve metadata for PID {pid}: {str(exception)}")
+            return {}  # Return an empty dictionary for this run
 
     def _create_dataset_url_by_PID(self, pid: str) -> str:  # pylint: disable=invalid-name
         """
@@ -120,18 +122,20 @@ class MetadataRetriever:
         Raises:
             Exception: If the token request fails.
         """
-        token_url = f"{self.url}/Users/login"
-        # Send a POST request to the token URL with the username and password
-        token_response = requests.post(
-            token_url,
-            headers=self.headers,
-            json={"username": self.username, "password": self.password},
-            timeout=10,
-        )
-
-        # If the token request is successful, return the access token from the response
-        if token_response.ok:
+        try:
+            token_url = f"{self.url}/Users/login"
+            # Send a POST request to the token URL with the username and password
+            token_response = requests.post(
+                token_url,
+                headers=self.headers,
+                json={"username": self.username, "password": self.password},
+                timeout=10,
+            )
+            token_response.raise_for_status()
+            # If the token request is successful, return the access token from the response
             return token_response.json()["id"]
 
-        # If the request fails, raise an exception with the error message
-        raise ConnectionError(f"{token_response.text}")
+            # Otherwise issue warning
+        except requests.exceptions.RequestException as exception:
+            warnings.warn(f"Failed to retrieve authentication token: {str(exception)}")
+            return ""  # Return an empty string if token retrieval fails
diff --git a/sed/loader/mpes/loader.py b/sed/loader/mpes/loader.py
index d1126a73fe0a42038ca46684c0f1e579a5aca69e..965abe91f66d6d3ec71690c50508291f21ed9ddf 100644
--- a/sed/loader/mpes/loader.py
+++ b/sed/loader/mpes/loader.py
@@ -4,6 +4,7 @@ Mostly ported from https://github.com/mpes-kit/mpes.
 @author: L. Rettig
 """
 import datetime
+import glob
 import json
 import os
 import urllib
@@ -19,6 +20,7 @@ import dask.dataframe as ddf
 import h5py
 import numpy as np
 import scipy.interpolate as sint
+from natsort import natsorted
 
 from sed.loader.base.loader import BaseLoader
 
@@ -362,14 +364,30 @@ class MpesLoader(BaseLoader):
             Tuple[ddf.DataFrame, dict]: Dask dataframe and metadata read from specified
             files.
         """
-        # pylint: disable=duplicate-code
-        super().read_dataframe(
-            files=files,
-            folders=folders,
-            runs=runs,
-            ftype=ftype,
-            metadata=metadata,
-        )
+        # if runs is provided, try to locate the respective files relative to the provided folder.
+        if runs is not None:  # pylint: disable=duplicate-code
+            files = []
+            if isinstance(runs, (str, int)):
+                runs = [runs]
+            for run in runs:
+                files.extend(
+                    self.get_files_from_run_id(run_id=run, folders=folders, extension=ftype),
+                )
+            self.runs = list(runs)
+            super().read_dataframe(
+                files=files,
+                ftype=ftype,
+                metadata=metadata,
+            )
+        else:
+            # pylint: disable=duplicate-code
+            super().read_dataframe(
+                files=files,
+                folders=folders,
+                runs=runs,
+                ftype=ftype,
+                metadata=metadata,
+            )
 
         hdf5_groupnames = kwds.pop(
             "hdf5_groupnames",
@@ -425,7 +443,7 @@ class MpesLoader(BaseLoader):
         self,
         run_id: str,
         folders: Union[str, Sequence[str]] = None,
-        extension: str = None,
+        extension: str = "h5",
         **kwds,
     ) -> List[str]:
         """Locate the files for a given run identifier.
@@ -433,14 +451,37 @@ class MpesLoader(BaseLoader):
         Args:
             run_id (str): The run identifier to locate.
             folders (Union[str, Sequence[str]], optional): The directory(ies) where the raw
-                data is located. Defaults to None.
+                data is located. Defaults to config["core"]["base_folder"]
             extension (str, optional): The file extension. Defaults to "h5".
             kwds: Keyword arguments
 
         Return:
-            str: Path to the location of run data.
+            List[str]: List of file path strings to the location of run data.
         """
-        raise NotImplementedError
+        if folders is None:
+            folders = self._config["core"]["paths"]["data_raw_dir"]
+
+        if isinstance(folders, str):
+            folders = [folders]
+
+        files: List[str] = []
+        for folder in folders:
+            run_files = natsorted(
+                glob.glob(
+                    folder + "/**/Scan" + str(run_id).zfill(4) + "_*." + extension,
+                    recursive=True,
+                ),
+            )
+            files.extend(run_files)
+
+        # Check if any files are found
+        if not files:
+            raise FileNotFoundError(
+                f"No files found for run {run_id} in directory {str(folders)}",
+            )
+
+        # Return the list of found files
+        return files
 
     def gather_metadata(
         self,
diff --git a/tests/calibrator/test_delay.py b/tests/calibrator/test_delay.py
index b6ac174b7867996e7dbd076aa5764e7e87f98bd9..e702a7cc238e443b564afb939dac992bd32d4fb8 100644
--- a/tests/calibrator/test_delay.py
+++ b/tests/calibrator/test_delay.py
@@ -11,11 +11,23 @@ from sed.loader.loader_interface import get_loader
 
 package_dir = os.path.dirname(find_spec("sed").origin)
 file = package_dir + "/../tests/data/loader/mpes/Scan0030_2.h5"
-config = parse_config(package_dir + "/../tests/data/config/config.yaml")
 
 
 def test_delay_parameters_from_file():
     """Test the option to extract the delay parameters from a file"""
+    config = parse_config(
+        config={
+            "core": {"loader": "mpes"},
+            "delay": {
+                "p1_key": "@trARPES:DelayStage:p1",
+                "p2_key": "@trARPES:DelayStage:p2",
+                "t0_key": "@trARPES:DelayStage:t0",
+            },
+        },
+        folder_config={},
+        user_config={},
+        system_config={},
+    )
     df, _ = get_loader(loader_name="mpes", config=config).read_dataframe(
         files=[file],
         collect_metadata=False,
@@ -33,6 +45,12 @@ def test_delay_parameters_from_file():
 def test_delay_parameters_from_delay_range():
     """Test the option to extract the delay parameters from a delay range"""
     # from keywords
+    config = parse_config(
+        config={"core": {"loader": "mpes"}},
+        folder_config={},
+        user_config={},
+        system_config={},
+    )
     df, _ = get_loader(loader_name="mpes", config=config).read_dataframe(
         files=[file],
         collect_metadata=False,
@@ -60,6 +78,12 @@ def test_delay_parameters_from_delay_range():
 def test_delay_parameters_from_delay_range_mm():
     """Test the option to extract the delay parameters from a mm range + t0"""
     # from keywords
+    config = parse_config(
+        config={"core": {"loader": "mpes"}},
+        folder_config={},
+        user_config={},
+        system_config={},
+    )
     df, _ = get_loader(loader_name="mpes", config=config).read_dataframe(
         files=[file],
         collect_metadata=False,
diff --git a/tests/calibrator/test_energy.py b/tests/calibrator/test_energy.py
index 2cd0f8aa1e9271fa21d97772de12eeec5ec6fbd7..ba931cc25b0744625d74be101fb21e7ffb47635a 100644
--- a/tests/calibrator/test_energy.py
+++ b/tests/calibrator/test_energy.py
@@ -21,7 +21,6 @@ package_dir = os.path.dirname(find_spec("sed").origin)
 df_folder = package_dir + "/../tests/data/loader/mpes/"
 folder = package_dir + "/../tests/data/calibrator/"
 files = glob.glob(df_folder + "*.h5")
-config = parse_config(package_dir + "/../tests/data/config/config.yaml")
 
 traces_list = []
 with open(folder + "traces.csv", newline="", encoding="utf-8") as csvfile:
@@ -39,6 +38,12 @@ with open(folder + "biases.csv", newline="", encoding="utf-8") as csvfile:
 
 def test_bin_data_and_read_biases_from_files():
     """Test binning the data and extracting the bias values from the files"""
+    config = parse_config(
+        config={"dataframe": {"tof_binning": 2}, "energy": {"bias_key": "@KTOF:Lens:Sample:V"}},
+        folder_config={},
+        user_config={},
+        system_config={},
+    )
     ec = EnergyCalibrator(
         config=config,
         loader=get_loader("mpes", config=config),
@@ -49,10 +54,35 @@ def test_bin_data_and_read_biases_from_files():
     assert ec.tof.ndim == 1
     assert ec.biases.ndim == 1
     assert len(ec.biases) == 2
+    default_config = parse_config(config={}, folder_config={}, user_config={}, system_config={})
+    ec = EnergyCalibrator(
+        config=default_config,
+        loader=get_loader("mpes", config=default_config),
+    )
+    with pytest.raises(ValueError):
+        ec.bin_data(data_files=files)
+    faulty_config = parse_config(
+        config={"energy": {"bias_key": "@KTOF:Lens:Sample"}},
+        folder_config={},
+        user_config={},
+        system_config={},
+    )
+    ec = EnergyCalibrator(
+        config=faulty_config,
+        loader=get_loader("mpes", config=faulty_config),
+    )
+    with pytest.raises(ValueError):
+        ec.bin_data(data_files=files)
 
 
 def test_energy_calibrator_from_arrays_norm():
     """Test loading the energy, bias and tof traces into the class"""
+    config = parse_config(
+        config={"dataframe": {"tof_binning": 2}},
+        folder_config={},
+        user_config={},
+        system_config={},
+    )
     ec = EnergyCalibrator(
         config=config,
         loader=get_loader("mpes", config=config),
@@ -67,6 +97,12 @@ def test_energy_calibrator_from_arrays_norm():
 
 def test_feature_extract():
     """Test generating the ranges, and extracting the features"""
+    config = parse_config(
+        config={"dataframe": {"tof_binning": 2}},
+        folder_config={},
+        user_config={},
+        system_config={},
+    )
     rand = [int((i + 1) * (20 + np.random.random() * 10)) for i in range(11)]
     traces_rand = np.zeros((len(rand), traces.shape[1]))
     for i, rnd in enumerate(rand):
@@ -81,7 +117,7 @@ def test_feature_extract():
         loader=get_loader("mpes", config=config),
     )
     ec.load_data(biases=biases, traces=traces_rand, tof=tof)
-    ec.add_features(ranges=rng, ref_id=ref_id)
+    ec.add_ranges(ranges=rng, ref_id=ref_id)
     for pos, feat_rng in zip(rand, ec.featranges):
         assert feat_rng[0] < (tof[1] - tof[0]) * pos + 65000 < feat_rng[1]
     ec.feature_extract()
@@ -92,6 +128,38 @@ def test_feature_extract():
     )
 
 
+def test_adjust_ranges():
+    """Test the interactive function for adjusting the feature ranges"""
+    config = parse_config(
+        config={"dataframe": {"tof_binning": 2}},
+        folder_config={},
+        user_config={},
+        system_config={},
+    )
+    rand = [int((i + 1) * (20 + np.random.random() * 10)) for i in range(11)]
+    traces_rand = np.zeros((len(rand), traces.shape[1]))
+    for i, rnd in enumerate(rand):
+        traces_rand[i, rnd:] = traces[0, 0:-rnd]
+    ref_id = np.random.randint(0, 10)
+    rng = (
+        64500 + (tof[1] - tof[0]) * rand[ref_id],
+        65300 + (tof[1] - tof[0]) * rand[ref_id],
+    )
+    ec = EnergyCalibrator(
+        config=config,
+        loader=get_loader("mpes", config=config),
+    )
+    ec.load_data(biases=biases, traces=traces_rand, tof=tof)
+    ec.adjust_ranges(ranges=rng, ref_id=ref_id, apply=True)
+    for pos, feat_rng in zip(rand, ec.featranges):
+        assert feat_rng[0] < (tof[1] - tof[0]) * pos + 65000 < feat_rng[1]
+    diff = ec.peaks[0, 0] - ((tof[1] - tof[0]) * rand[0] + 65000)
+    np.testing.assert_allclose(
+        ec.peaks[:, 0],
+        ((tof[1] - tof[0]) * np.asarray(rand) + 65000) + diff,
+    )
+
+
 energy_scales = ["kinetic", "binding"]
 calibration_methods = ["lmfit", "lstsq", "lsqr"]
 
@@ -108,6 +176,12 @@ def test_calibrate_append(energy_scale: str, calibration_method: str):
         energy_scale (str): tpye of energy scaling
         calibration_method (str): method used for ralibration
     """
+    config = parse_config(
+        config={"dataframe": {"tof_binning": 2}},
+        folder_config={},
+        user_config={},
+        system_config={},
+    )
     loader = get_loader(loader_name="mpes", config=config)
     df, _ = loader.read_dataframe(folders=df_folder, collect_metadata=False)
     ec = EnergyCalibrator(config=config, loader=loader)
@@ -115,7 +189,7 @@ def test_calibrate_append(energy_scale: str, calibration_method: str):
     ec.normalize()
     rng = (66100, 67000)
     ref_id = 5
-    ec.add_features(ranges=rng, ref_id=ref_id)
+    ec.add_ranges(ranges=rng, ref_id=ref_id)
     ec.feature_extract()
     refid = 4
     e_ref = -0.5
@@ -149,7 +223,7 @@ calib_dicts = [{"d": 1, "t0": 0, "E0": 0}, {"coeffs": [1, 2, 3], "E0": 0}]
     "calib_type, calib_dict",
     zip(calib_types, calib_dicts),
 )
-def test_apply_correction_from_dict_kwds(calib_type: str, calib_dict: dict):
+def test_append_energy_axis_from_dict_kwds(calib_type: str, calib_dict: dict):
     """Function to test if the energy calibration is correctly applied using a dict or
     kwd parameters.
 
@@ -157,6 +231,7 @@ def test_apply_correction_from_dict_kwds(calib_type: str, calib_dict: dict):
         calib_type (str): type of calibration.
         calib_dict (dict): Dictionary with calibration parameters.
     """
+    config = parse_config(config={}, folder_config={}, user_config={}, system_config={})
     loader = get_loader(loader_name="mpes", config=config)
     # from dict
     df, _ = loader.read_dataframe(folders=df_folder, collect_metadata=False)
@@ -179,6 +254,21 @@ def test_apply_correction_from_dict_kwds(calib_type: str, calib_dict: dict):
     assert metadata["calibration"]["calib_type"] == calib_type
 
 
+def test_append_energy_axis_raises():
+    """Test if apply_correction raises the correct errors"""
+    config = parse_config(config={}, folder_config={}, user_config={}, system_config={})
+    loader = get_loader(loader_name="mpes", config=config)
+    df, _ = loader.read_dataframe(folders=df_folder, collect_metadata=False)
+    ec = EnergyCalibrator(config=config, loader=loader)
+    with pytest.raises(ValueError):
+        df, _ = ec.append_energy_axis(df, calibration={"d": 1, "t0": 0})
+    with pytest.raises(NotImplementedError):
+        df, _ = ec.append_energy_axis(
+            df,
+            calibration={"d": 1, "t0": 0, "E0": 0, "calib_type": "invalid"},
+        )
+
+
 amplitude = 2.5  # pylint: disable=invalid-name
 center = (730, 730)
 sample = np.array(
@@ -228,6 +318,8 @@ def test_energy_correction(correction_type: str, correction_kwd: dict):
         correction_type (str): type of correction to test
         correction_kwd (dict): parameters to pass to the function
     """
+    # From keywords
+    config = parse_config(config={}, user_config={}, system_config={})
     sample_df = pd.DataFrame(sample, columns=columns)
     ec = EnergyCalibrator(
         config=config,
@@ -250,11 +342,95 @@ def test_energy_correction(correction_type: str, correction_kwd: dict):
     assert t[3] < t[4]
     assert t[1] == t[4]
 
+    assert ec.correction["correction_type"] == correction_type
+    assert ec.correction["amplitude"] == amplitude
+    assert ec.correction["center"] == center
+
     for key, value in ec.correction.items():
         np.testing.assert_equal(
             metadata["correction"][key],
             value,
         )
+    # From dict
+    config = parse_config(config={}, user_config={}, system_config={})
+    sample_df = pd.DataFrame(sample, columns=columns)
+    ec = EnergyCalibrator(
+        config=config,
+        loader=get_loader("mpes", config=config),
+    )
+    correction: Dict[Any, Any] = {
+        "correction_type": correction_type,
+        "amplitude": amplitude,
+        "center": center,
+        **correction_kwd,
+    }
+    ec.adjust_energy_correction(
+        image=image,
+        tof_fermi=tof_fermi,
+        apply=True,
+        **correction,
+    )
+    df, metadata = ec.apply_energy_correction(sample_df)
+    t = df[config["dataframe"]["corrected_tof_column"]]
+    assert t[0] == t[2]
+    assert t[0] < t[1]
+    assert t[3] == t[5]
+    assert t[3] < t[4]
+    assert t[1] == t[4]
+
+    assert ec.correction["correction_type"] == correction["correction_type"]
+    assert ec.correction["amplitude"] == correction["amplitude"]
+    assert ec.correction["center"] == correction["center"]
+
+    for key, value in ec.correction.items():
+        np.testing.assert_equal(
+            metadata["correction"][key],
+            value,
+        )
+
+
+@pytest.mark.parametrize(
+    "correction_type",
+    correction_types,
+)
+def test_adjust_energy_correction_raises(
+    correction_type: str,
+):
+    """Function to test if the adjust_energy_correction function raises the correct errors.
+
+    Args:
+        correction_type (str): type of correction to test
+    """
+    config = parse_config(config={}, folder_config={}, user_config={}, system_config={})
+    ec = EnergyCalibrator(
+        config=config,
+        loader=get_loader("mpes", config=config),
+    )
+    correction_dict: Dict[str, Any] = {
+        "correction_type": correction_type,
+        "amplitude": amplitude,
+        "center": center,
+    }
+    with pytest.raises(ValueError):
+        ec.adjust_energy_correction(
+            image=image,
+            **correction_dict,
+            apply=True,
+        )
+    if correction_type == "Lorentzian_asymmetric":
+        correction_dict = {
+            "correction_type": correction_type,
+            "amplitude": amplitude,
+            "center": center,
+            "gamma": 900,
+        }
+        ec.adjust_energy_correction(
+            image=image,
+            **correction_dict,
+            apply=True,
+        )
+        assert ec.correction["gamma2"] == correction_dict["gamma"]
+        assert ec.correction["amplitude2"] == correction_dict["amplitude"]
 
 
 @pytest.mark.parametrize(
@@ -272,7 +448,7 @@ def test_energy_correction_from_dict_kwds(
         correction_type (str): type of correction to test
         correction_kwd (dict): parameters to pass to the function
     """
-    # from dict
+    config = parse_config(config={}, folder_config={}, user_config={}, system_config={})
     sample_df = pd.DataFrame(sample, columns=columns)
     ec = EnergyCalibrator(
         config=config,
@@ -320,3 +496,45 @@ def test_energy_correction_from_dict_kwds(
             metadata["correction"][key],
             value,
         )
+
+
+@pytest.mark.parametrize(
+    "correction_type",
+    correction_types,
+)
+def test_apply_energy_correction_raises(
+    correction_type: str,
+):
+    """Function to test if the apply_energy_correction raises the correct errors.
+
+    Args:
+        correction_type (str): type of correction to test
+    """
+    config = parse_config(config={}, folder_config={}, user_config={}, system_config={})
+    sample_df = pd.DataFrame(sample, columns=columns)
+    ec = EnergyCalibrator(
+        config=config,
+        loader=get_loader("mpes", config=config),
+    )
+    correction_dict: Dict[str, Any] = {
+        "correction_type": correction_type,
+        "amplitude": amplitude,
+        "center": center,
+    }
+    with pytest.raises(ValueError):
+        df, _ = ec.apply_energy_correction(
+            sample_df,
+            correction=correction_dict,
+        )
+    if correction_type == "Lorentzian_asymmetric":
+        correction_dict = {
+            "correction_type": correction_type,
+            "amplitude": amplitude,
+            "center": center,
+            "gamma": 900,
+        }
+        df, _ = ec.apply_energy_correction(
+            sample_df,
+            correction=correction_dict,
+        )
+        assert config["dataframe"]["corrected_tof_column"] in df.columns
diff --git a/tests/calibrator/test_momentum.py b/tests/calibrator/test_momentum.py
index e714fbfa513ed82505a44265dd8802d8c0171604..95cb2446a0b1ecdee681d9ae1fcc75d40d5bd043 100644
--- a/tests/calibrator/test_momentum.py
+++ b/tests/calibrator/test_momentum.py
@@ -11,8 +11,8 @@ import numpy as np
 import pytest
 
 from sed.calibrator.momentum import MomentumCorrector
-from sed.core.config import parse_config
 from sed.core import SedProcessor
+from sed.core.config import parse_config
 from sed.loader.loader_interface import get_loader
 
 # pylint: disable=duplicate-code
@@ -20,7 +20,6 @@ package_dir = os.path.dirname(find_spec("sed").origin)
 df_folder = package_dir + "/../tests/data/loader/mpes/"
 folder = package_dir + "/../tests/data/calibrator/"
 files = glob.glob(df_folder + "*.h5")
-config = parse_config(package_dir + "/../tests/data/config/config.yaml")
 
 momentum_map_list = []
 with open(
@@ -36,7 +35,13 @@ momentum_map = np.asarray(momentum_map_list).T
 
 def test_bin_data_and_slice_image():
     """Test binning the data and slicing of the image"""
-    sed_processor = SedProcessor(config=config)
+    config = parse_config(
+        config={"core": {"loader": "mpes"}},
+        folder_config={},
+        user_config={},
+        system_config={},
+    )
+    sed_processor = SedProcessor(config=config, system_config={})
     sed_processor.load(files=files)
     sed_processor.bin_and_load_momentum_calibration(
         plane=33,
@@ -48,6 +53,12 @@ def test_bin_data_and_slice_image():
 
 def test_feature_extract():
     """Testextracting the feature from a 2D slice"""
+    config = parse_config(
+        config={"core": {"loader": "mpes"}},
+        folder_config={},
+        user_config={},
+        system_config={},
+    )
     mc = MomentumCorrector(config=config)
     mc.load_data(
         data=momentum_map,
@@ -68,6 +79,12 @@ def test_splinewarp(include_center: bool):
     Args:
         include_center (bool): Option to include the center point.
     """
+    config = parse_config(
+        config={"core": {"loader": "mpes"}},
+        folder_config={},
+        user_config={},
+        system_config={},
+    )
     mc = MomentumCorrector(config=config)
     mc.load_data(
         data=momentum_map,
@@ -87,13 +104,19 @@ def test_splinewarp(include_center: bool):
     if not include_center:
         features = features[0:-1]
     mc.add_features(features=features, rotsym=6)
-    mc.spline_warp_estimate(include_center=include_center)
+    mc.spline_warp_estimate(use_center=include_center)
     assert mc.cdeform_field.shape == mc.rdeform_field.shape == mc.image.shape
     assert len(mc.ptargs) == len(mc.prefs)
 
 
 def test_pose_correction():
     """Test the adjustment of the pose correction."""
+    config = parse_config(
+        config={"core": {"loader": "mpes"}},
+        folder_config={},
+        user_config={},
+        system_config={},
+    )
     mc = MomentumCorrector(config=config)
     mc.load_data(
         data=momentum_map,
@@ -107,6 +130,12 @@ def test_pose_correction():
 
 def test_apply_correction():
     """Test the application of the distortion correction to the dataframe."""
+    config = parse_config(
+        config={"core": {"loader": "mpes"}},
+        folder_config={},
+        user_config={},
+        system_config={},
+    )
     df, _ = get_loader(loader_name="mpes", config=config).read_dataframe(
         folders=df_folder,
         collect_metadata=False,
@@ -151,8 +180,8 @@ transformations_list = [
         "angle": np.random.randint(1, 50),
     },
     {
-        "ytrans": np.random.randint(0, 50),
-        "angle": np.random.randint(0, 50),
+        "ytrans": np.random.randint(1, 50),
+        "angle": np.random.randint(1, 50),
     },
     {
         "xtrans": np.random.randint(1, 50),
@@ -214,6 +243,12 @@ def test_apply_registration(
     depends_on: Dict[Any, Any],
 ):
     """Test the application of the distortion correction to the dataframe."""
+    config = parse_config(
+        config={"core": {"loader": "mpes"}},
+        folder_config={},
+        user_config={},
+        system_config={},
+    )
     df, _ = get_loader(loader_name="mpes", config=config).read_dataframe(
         folders=df_folder,
         collect_metadata=False,
@@ -223,8 +258,19 @@ def test_apply_registration(
         data=momentum_map,
         bin_ranges=[(-256, 1792), (-256, 1792)],
     )
-    mc.add_features()
-    mc.spline_warp_estimate()  # use spline warp with default parameters
+    features = np.array(
+        [
+            [203.2, 341.96],
+            [299.16, 345.32],
+            [350.25, 243.70],
+            [304.38, 149.88],
+            [199.52, 152.48],
+            [154.28, 242.27],
+            [248.29, 248.62],
+        ],
+    )
+    mc.add_features(features=features, rotsym=6)
+    mc.spline_warp_estimate()
     mc.pose_adjustment(**transformations, apply=True)
     df, metadata = mc.apply_corrections(df=df)
     assert "Xm" in df.columns
@@ -258,6 +304,12 @@ def test_momentum_calibration_equiscale():
     """Test the calibration using one point and the k-distance,
     and application to the dataframe.
     """
+    config = parse_config(
+        config={"core": {"loader": "mpes"}},
+        folder_config={},
+        user_config={},
+        system_config={},
+    )
     df, _ = get_loader(loader_name="mpes", config=config).read_dataframe(
         folders=df_folder,
         collect_metadata=False,
@@ -283,6 +335,12 @@ def test_momentum_calibration_equiscale():
 
 def test_momentum_calibration_two_points():
     """Test the calibration using two k-points, and application to the dataframe."""
+    config = parse_config(
+        config={"core": {"loader": "mpes"}},
+        folder_config={},
+        user_config={},
+        system_config={},
+    )
     df, _ = get_loader(loader_name="mpes", config=config).read_dataframe(
         folders=df_folder,
         collect_metadata=False,
diff --git a/tests/data/config/config.json b/tests/data/config/config.json
index fb313378f225d6eae13d43b7da6711f3c1cca293..58bedf133e34a8c9209fc5d95655b40354ad6094 100644
--- a/tests/data/config/config.json
+++ b/tests/data/config/config.json
@@ -1,571 +1,21 @@
 {
-  "core": {
-    "loader": "mpes",
-    "use_copy_tool": false,
-    "copy_tool_source": "/path/to/data/",
-    "copy_tool_dest": "/path/to//localDataStore/",
-    "copy_tool_kwds": {
-      "ntasks": 20,
-      "gid": 1001
-    }
-  },
-  "dataframe": {
-    "hdf5_groupnames": [
-      "Stream_0",
-      "Stream_1",
-      "Stream_2",
-      "Stream_4"
-    ],
-    "hdf5_aliases": {
-      "Stream_0": "X",
-      "Stream_1": "Y",
-      "Stream_2": "t",
-      "Stream_4": "ADC"
-    },
-    "time_stamp_alias": "timeStamps",
-    "ms_markers_group": "msMarkers",
-    "first_event_time_stamp_key": "FirstEventTimeStamp",
-    "jitter_cols": [
-      "X",
-      "Y",
-      "t",
-      "ADC"
-    ],
-    "x_column": "X",
-    "y_column": "Y",
-    "tof_column": "t",
-    "corrected_tof_column": "tm",
-    "adc_column": "ADC",
-    "energy_column": "energy",
-    "corrected_x_column": "Xm",
-    "corrected_y_column": "Ym",
-    "kx_column": "kx",
-    "ky_column": "ky",
-    "delay_column": "delay",
-    "tof_binwidth": 4.125e-12,
-    "tof_binning": 2,
-    "units": {
-      "X": "step",
-      "Y": "step",
-      "t": "step",
-      "tof_voltage": "V",
-      "extractor_oltage": "V",
-      "extractor_current": "A",
-      "cryo_temperature": "K",
-      "sample_temperature": "K",
-      "dld_time": "ns",
-      "delay": "ps",
-      "timeStamp": "s",
-      "energy": "eV",
-      "E": "eV",
-      "kx": "1/A",
-      "ky": "1/A"
-    }
-  },
-  "energy": {
-    "bins": 1000,
-    "ranges": [
-      128000,
-      138000
-    ],
-    "bias_key": "@KTOF:Lens:Sample:V",
-    "normalize": true,
-    "normalize_span": 7,
-    "normalize_order": 1,
-    "fastdtw_radius": 2,
-    "peak_window": 7,
-    "calibration_method": "lmfit",
-    "energy_scale": "kinetic",
-    "tof_fermi": 132250,
-    "x_width": [
-      -20,
-      20
-    ],
-    "y_width": [
-      -20,
-      20
-    ],
-    "tof_width": [
-      -300,
-      500
-    ],
-    "color_clip": 300,
-    "correction": {
-      "correction_type": "Lorentzian",
-      "amplitude": 2.5,
-      "center": [
-        730.0,
-        730.0
-      ],
-      "gamma": 920.0,
-      "sigma": 700.0,
-      "diameter": 3000.0
-    },
-    "calibration": {
-      "d": 1.058206295066418,
-      "t0": 7.684410678887588e-07,
-      "E0": -30.440035779171833,
-      "energy_scale": "kinetic"
-    }
-  },
-  "momentum": {
-    "axes": [
-      "@x_column",
-      "@y_column",
-      "@tof_column"
-    ],
-    "bins": [
-      512,
-      512,
-      300
-    ],
-    "ranges": [
-      [
-        -256,
-        1792
-      ],
-      [
-        -256,
-        1792
-      ],
-      [
-        66000,
-        68000
-      ]
-    ],
-    "detector_ranges": [
-      [
-        0,
-        2048
-      ],
-      [
-        0,
-        2048
-      ]
-    ],
-    "center_pixel": [
-      256,
-      256
-    ],
-    "sigma": 5,
-    "fwhm": 8,
-    "sigma_radius": 1,
-    "center_det": "centroidnn",
-    "calibration": {
-      "kx_scale": 0.012389400615413859,
-      "ky_scale": 0.012389400615413859,
-      "x_center": 256.0,
-      "y_center": 256.0,
-      "rstart": -256.0,
-      "cstart": -256.0,
-      "rstep": 4.0,
-      "cstep": 4.0
-    },
-    "correction": {
-      "feature_points": [
-        [
-          203.2,
-          341.96
-        ],
-        [
-          299.16,
-          345.32
-        ],
+  "entry1": {
+    "nested1": {
+      "array1": [
         [
-          350.25,
-          243.7
+          "1 2 3 4 5"
         ],
         [
-          304.38,
-          149.88
-        ],
-        [
-          199.52,
-          152.48
-        ],
-        [
-          154.28,
-          242.27
-        ],
-        [
-          248.29,
-          248.62
+          "2 3 4 5 6"
         ]
       ]
     }
   },
-  "delay": {
-    "adc_range": [
-      1900,
-      25600
-    ],
-    "adc_binning": 2,
-    "p1_key": "@trARPES:DelayStage:p1",
-    "p2_key": "@trARPES:DelayStage:p2",
-    "t0_key": "@trARPES:DelayStage:t0"
-  },
-  "binning": {
-    "hist_mode": "numba",
-    "mode": "fast",
-    "pbar": true,
-    "num_cores": 20,
-    "threads_per_worker": 4,
-    "threadpool_API": "blas"
-  },
-  "histogram": {
-    "bins": [
-      80,
-      80,
-      80,
-      80
-    ],
-    "axes": [
-      "X",
-      "Y",
-      "t",
-      "ADC"
-    ],
-    "ranges": [
-      [
-        0,
-        1800
-      ],
-      [
-        0,
-        1800
-      ],
-      [
-        65000,
-        69000
-      ],
-      [
-        0,
-        9000
-      ]
-    ]
-  },
-  "metadata": {
-    "epics_pvs": [
-      "KTOF:Lens:Extr:I",
-      "trARPES:Carving:TEMP_RBV",
-      "trARPES:XGS600:PressureAC:P_RD",
-      "KTOF:Lens:UDLD:V",
-      "KTOF:Lens:Sample:V",
-      "KTOF:Apertures:m1.RBV",
-      "KTOF:Apertures:m2.RBV",
-      "KTOF:Apertures:m3.RBV",
-      "trARPES:Carving:TRX.RBV",
-      "trARPES:Carving:TRY.RBV",
-      "trARPES:Carving:TRZ.RBV",
-      "trARPES:Carving:THT.RBV",
-      "trARPES:Carving:PHI.RBV",
-      "trARPES:Carving:OMG.RBV"
-    ],
-    "fa_in_channel": "KTOF:Apertures:m1.RBV",
-    "fa_hor_channel": "KTOF:Apertures:m2.RBV",
-    "ca_in_channel": "KTOF:Apertures:m3.RBV",
-    "aperture_config": {
-      "2018-01-23T19:35:15": {
-        "fa_size": {
-          "750": [
-            [
-              -3.0,
-              -1.4
-            ],
-            [
-              -5.4,
-              -4.6
-            ]
-          ],
-          "grid": [
-            [
-              -3.0,
-              -1.4
-            ],
-            [
-              0.15,
-              1.75
-            ]
-          ],
-          "1500": [
-            [
-              -3.0,
-              -1.4
-            ],
-            [
-              6.25,
-              7.75
-            ]
-          ],
-          "200": [
-            [
-              3.3,
-              4.4
-            ],
-            [
-              -5.4,
-              -4.6
-            ]
-          ],
-          "500": [
-            [
-              3.3,
-              4.4
-            ],
-            [
-              0.15,
-              1.75
-            ]
-          ],
-          "1000": [
-            [
-              3.3,
-              4.4
-            ],
-            [
-              6.25,
-              7.75
-            ]
-          ],
-          "20": [
-            [
-              9.6,
-              10.1
-            ],
-            [
-              -5.4,
-              -4.6
-            ]
-          ],
-          "50": [
-            [
-              9.6,
-              10.1
-            ],
-            [
-              0.15,
-              1.75
-            ]
-          ],
-          "100": [
-            [
-              9.6,
-              10.1
-            ],
-            [
-              6.25,
-              7.75
-            ]
-          ],
-          "open": [
-            [
-              -15,
-              -9.0
-            ],
-            [
-              -15,
-              -8.9
-            ]
-          ]
-        },
-        "ca_size": {
-          "50": [
-            8.0,
-            8.4
-          ],
-          "200": [
-            -0.5,
-            -0.9
-          ],
-          "100": [
-            3.4,
-            3.8
-          ],
-          "grid": [
-            -5.3,
-            -5.9
-          ],
-          "open": [
-            -12.0,
-            -8
-          ]
-        }
-      },
-      "2020-01-23T19:35:15": {
-        "fa_size": {
-          "750": [
-            [
-              -6.2,
-              -4.8
-            ],
-            [
-              5.0,
-              6.0
-            ]
-          ],
-          "grid": [
-            [
-              -6.2,
-              -4.8
-            ],
-            [
-              -0.7,
-              -0.3
-            ]
-          ],
-          "500": [
-            [
-              -6.2,
-              -4.8
-            ],
-            [
-              -7.0,
-              -6.0
-            ]
-          ],
-          "200": [
-            [
-              0.5,
-              0.9
-            ],
-            [
-              -0.7,
-              -0.3
-            ]
-          ],
-          "100": [
-            [
-              0.5,
-              0.9
-            ],
-            [
-              -7.0,
-              -6.0
-            ]
-          ],
-          "300": [
-            [
-              0.5,
-              0.9
-            ],
-            [
-              5.0,
-              6.0
-            ]
-          ],
-          "10": [
-            [
-              6.5,
-              6.9
-            ],
-            [
-              -7.0,
-              -6.0
-            ]
-          ],
-          "20": [
-            [
-              6.5,
-              6.9
-            ],
-            [
-              -0.7,
-              -0.3
-            ]
-          ],
-          "50": [
-            [
-              6.5,
-              6.9
-            ],
-            [
-              5.0,
-              6.0
-            ]
-          ],
-          "open": [
-            [
-              -15,
-              -8.5
-            ],
-            [
-              -15,
-              -8.9
-            ]
-          ]
-        },
-        "ca_size": {
-          "50": [
-            9.0,
-            11.0
-          ],
-          "300": [
-            -0.1,
-            0.1
-          ],
-          "200": [
-            0.7,
-            1.5
-          ],
-          "100": [
-            5.1,
-            5.9
-          ],
-          "grid": [
-            -5.5,
-            -5.2
-          ],
-          "open": [
-            -15,
-            -8.5
-          ]
-        }
-      }
-    },
-    "lens_mode_config": {
-      "6kV_kmodem4.0_20VTOF_v3.sav": {
-        "Extr": 6000.0,
-        "UCA": 1200,
-        "UFA": 600.0,
-        "Z1": 2452.9,
-        "Z2": 1489.9,
-        "A": 420.07,
-        "B": 2494.8,
-        "C": 489.2,
-        "D": 228.05,
-        "E": 113.82,
-        "F": 54.232,
-        "G": 20.0,
-        "H": 25.5,
-        "I": 36.0,
-        "TOF": 20.0,
-        "MCPfront": 20.0
-      },
-      "6kV_kmodem4.0_30VTOF_453ns_focus.sav": {
-        "Extr": 6000.0,
-        "UCA": 1200,
-        "UFA": 600.0,
-        "Z1": 2452.9,
-        "Z2": 1489.9,
-        "A": 403.07,
-        "B": 2500,
-        "C": 422.25,
-        "D": 208.88,
-        "E": 199.49,
-        "F": 68.735,
-        "G": 30.0,
-        "H": 30.0,
-        "I": 44.5,
-        "TOF": 30.0,
-        "MCPfront": 30.0
-      }
+  "entry2": {
+    "nested2": {
+      "string1": "abc",
+      "number1": 12,
+      "bool1": true
     }
-  },
-  "nexus": {
-    "reader": "mpes",
-    "definition": "NXmpes",
-    "input_files": [
-      "../tests/data/config/NXmpes_config.json"
-    ]
   }
 }
diff --git a/tests/data/config/config.yaml b/tests/data/config/config.yaml
index 33302181b27c380d35a24dd33d6c203a00ab5ee6..e6e46f9df6bbf667f4f6ffe748d49ebd3e30ad5e 100644
--- a/tests/data/config/config.yaml
+++ b/tests/data/config/config.yaml
@@ -1,204 +1,19 @@
-core:
-  loader: mpes
-  use_copy_tool: False
-  copy_tool_source: "/path/to/data/"
-  copy_tool_dest: "/path/to//localDataStore/"
-  copy_tool_kwds:
-    ntasks: 20
-    gid: 1001
-
-dataframe:
-  hdf5_groupnames: ["Stream_0", "Stream_1", "Stream_2", "Stream_4"]
-  hdf5_aliases:
-    Stream_0: "X"
-    Stream_1: "Y"
-    Stream_2: "t"
-    Stream_4: "ADC"
-  time_stamp_alias: "timeStamps"
-  ms_markers_group: "msMarkers"
-  first_event_time_stamp_key: "FirstEventTimeStamp"
-  jitter_cols: ["X", "Y", "t", "ADC"]
-  x_column: "X"
-  y_column: "Y"
-  tof_column: "t"
-  corrected_tof_column: "tm"
-  adc_column: "ADC"
-  energy_column: "energy"
-  corrected_x_column: "Xm"
-  corrected_y_column: "Ym"
-  kx_column: "kx"
-  ky_column: "ky"
-  delay_column: "delay"
-  tof_binwidth: 4.125e-12
-  tof_binning: 2
-  units:
-    X: 'step'
-    Y: 'step'
-    t: 'step'
-    tof_voltage: 'V'
-    extractor_oltage: 'V'
-    extractor_current: 'A'
-    cryo_temperature: 'K'
-    sample_temperature: 'K'
-    dld_time: 'ns'
-    delay: 'ps'
-    timeStamp: 's'
-    energy: 'eV'
-    E: 'eV'
-    kx: '1/A'
-    ky: '1/A'
-
-energy:
-  bins: 1000
-  ranges: [128000, 138000]
-  bias_key: "@KTOF:Lens:Sample:V"
-  normalize: True
-  normalize_span: 7
-  normalize_order: 1
-  fastdtw_radius: 2
-  peak_window: 7
-  calibration_method: "lmfit"
-  energy_scale: "kinetic"
-  tof_fermi: 132250
-  x_width: [-20, 20]
-  y_width: [-20, 20]
-  tof_width: [-300, 500]
-  color_clip: 300
-  correction:
-    correction_type: "Lorentzian"
-    amplitude: 2.5
-    center: [730.0, 730.0]
-    gamma: 920.0
-    sigma: 700.0
-    diameter: 3000.0
-  calibration:
-    d: 1.058206295066418
-    t0: 7.684410678887588e-07
-    E0: -30.440035779171833
-    energy_scale: "kinetic"
-
-momentum:
-  axes: ["@x_column", "@y_column", "@tof_column"]
-  bins: [512, 512, 300]
-  ranges: [[-256, 1792], [-256, 1792], [66000, 68000]]
-  detector_ranges: [[0, 2048], [0, 2048]]
-  center_pixel: [256, 256]
-  sigma: 5
-  fwhm: 8
-  sigma_radius: 1
-  center_det: "centroidnn"
-  calibration:
-    kx_scale: 0.012389400615413859
-    ky_scale: 0.012389400615413859
-    x_center: 256.0
-    y_center: 256.0
-    rstart: -256.
-    cstart: -256.
-    rstep: 4.0
-    cstep: 4.0
-  correction:
-    feature_points: [[203.2, 341.96], [299.16, 345.32], [350.25, 243.70], [304.38, 149.88], [199.52, 152.48], [154.28, 242.27], [248.29, 248.62]]
-
-delay:
-  adc_range: [1900, 25600]
-  adc_binning: 2
-  p1_key: "@trARPES:DelayStage:p1"
-  p2_key: "@trARPES:DelayStage:p2"
-  t0_key: "@trARPES:DelayStage:t0"
-
-binning:
-  hist_mode: "numba"
-  mode: "fast"
-  pbar: True
-  num_cores: 20
-  threads_per_worker: 4
-  threadpool_API: "blas"
-
-histogram:
-  bins: [80, 80, 80, 80]
-  axes: ["X", "Y", "t", "ADC"]
-  ranges: [[0, 1800], [0, 1800], [65000, 69000], [0, 9000], ]
-
-metadata:
-  epics_pvs: ["KTOF:Lens:Extr:I", "trARPES:Carving:TEMP_RBV", "trARPES:XGS600:PressureAC:P_RD", "KTOF:Lens:UDLD:V", "KTOF:Lens:Sample:V", "KTOF:Apertures:m1.RBV", "KTOF:Apertures:m2.RBV", "KTOF:Apertures:m3.RBV", "trARPES:Carving:TRX.RBV", "trARPES:Carving:TRY.RBV", "trARPES:Carving:TRZ.RBV", "trARPES:Carving:THT.RBV", "trARPES:Carving:PHI.RBV", "trARPES:Carving:OMG.RBV"]
-  fa_in_channel: 'KTOF:Apertures:m1.RBV'
-  fa_hor_channel: 'KTOF:Apertures:m2.RBV'
-  ca_in_channel: 'KTOF:Apertures:m3.RBV'
-  aperture_config:
-    "2018-01-23T19:35:15":
-      fa_size:
-        '750': [[-3.0, -1.4], [-5.4, -4.6]]
-        grid: [[-3.0, -1.4], [0.15, 1.75]]
-        '1500': [[-3.0, -1.4], [6.25, 7.75]]
-        '200': [[3.3, 4.4], [-5.4, -4.6]]
-        '500': [[3.3, 4.4], [0.15, 1.75]]
-        '1000': [[3.3, 4.4], [6.25, 7.75]]
-        '20': [[9.6, 10.1], [-5.4, -4.6]]
-        '50': [[9.6, 10.1], [0.15, 1.75]]
-        '100': [[9.6, 10.1], [6.25, 7.75]]
-        open: [[-15, -9.0], [-15, -8.9]]
-      ca_size:
-        '50': [8.0, 8.4]
-        '200': [-0.5, -0.9]
-        '100': [3.4, 3.8]
-        grid: [-5.3, -5.9]
-        open: [-12.0, -8]
-    "2020-01-23T19:35:15":
-      fa_size:
-        '750': [[-6.2, -4.8], [5.0, 6.0]]
-        grid: [[-6.2, -4.8], [-0.7, -0.3]]
-        '500': [[-6.2, -4.8], [-7.0, -6.0]]
-        '200': [[0.5, 0.9], [-0.7, -0.3]]
-        '100': [[0.5, 0.9], [-7.0, -6.0]]
-        '300': [[0.5, 0.9], [5.0, 6.0]]
-        '10': [[6.5, 6.9], [-7.0, -6.0]]
-        '20': [[6.5, 6.9], [-0.7, -0.3]]
-        '50': [[6.5, 6.9], [5.0, 6.0]]
-        open: [[-15, -8.5], [-15, -8.9]]
-      ca_size:
-        '50': [9.0, 11.0]
-        '300': [-0.1, 0.1]
-        '200': [0.7, 1.5]
-        '100': [5.1, 5.9]
-        grid: [-5.5, -5.2]
-        open: [-15, -8.5]
-  lens_mode_config:
-    "6kV_kmodem4.0_20VTOF_v3.sav":
-      Extr: 6000.0
-      UCA: 1200
-      UFA: 600.0
-      Z1: 2452.9
-      Z2: 1489.9
-      A: 420.07
-      B: 2494.8
-      C: 489.2
-      D: 228.05
-      E: 113.82
-      F: 54.232
-      G: 20.0
-      H: 25.5
-      I: 36.0
-      TOF: 20.0
-      MCPfront: 20.0
-    "6kV_kmodem4.0_30VTOF_453ns_focus.sav":
-      Extr: 6000.0
-      UCA: 1200
-      UFA: 600.0
-      Z1: 2452.9
-      Z2: 1489.9
-      A: 403.07
-      B: 2500
-      C: 422.25
-      D: 208.88
-      E: 199.49
-      F: 68.735
-      G: 30.0
-      H: 30.0
-      I: 44.5
-      TOF: 30.0
-      MCPfront: 30.0
-
-nexus:
-  reader: "mpes"
-  definition: "NXmpes"
-  input_files: ["../tests/data/config/NXmpes_config.json"]
+entry1:
+  nested1:
+    array1:
+      - - 1
+          2
+          3
+          4
+          5
+      - - 2
+          3
+          4
+          5
+          6
+
+entry2:
+  nested2:
+    string1: "abc"
+    number1: 12
+    bool1: True
diff --git a/tests/data/loader/flash/config.yaml b/tests/data/loader/flash/config.yaml
index d6e004c3675bc1dc70c05cd21930b943a1077a8a..9a6149758b2b96272c5968874c272d8ef9bf7606 100644
--- a/tests/data/loader/flash/config.yaml
+++ b/tests/data/loader/flash/config.yaml
@@ -1,35 +1,62 @@
+# This file contains the default configuration for the flash loader.
+
 core:
+  # defines the loader
   loader: flash
+
+  # the beamline where experiment took place
+  beamline: pg2
+
+  # The paths to the raw and parquet data directories.
   paths:
     data_raw_dir: "tests/data/loader/flash/"
     data_parquet_dir: "tests/data/loader/flash/parquet"
+    
+  # These can be replaced by beamtime_id and year to automatically
+  # find the folders on the desy cluster
+  
+  # beamtime_id: xxxxxxxx
+  # year: 20xx
 
 dataframe:
+  # The offset correction to the pulseId
   ubid_offset: 5
+
+  # The name of the DAQ system to use. Necessary to resolve the filenames/paths.
   daq: fl1user3
+
+  # The channels to load.
+  # channels have the following structure:
+  # channelAlias:
+  #   format: per_pulse/per_electron/per_train
+  #   group_name: the hdf5 group path
+  #   slice: if the group contains multidim data, where to slice
+  
   channels:
+    # pulse ID is a necessary channel for using the loader.
     pulseId:
-      axis: 1
       format: per_electron
       group_name: "/uncategorised/FLASH.EXP/HEXTOF.DAQ/DLD1/"
       slice: 2
+
     dldPosX:
-      axis: 1
       format: per_electron
       group_name: "/uncategorised/FLASH.EXP/HEXTOF.DAQ/DLD1/"
       slice: 1
+      
     dldPosY:
-      axis: 1
       format: per_electron
       group_name: "/uncategorised/FLASH.EXP/HEXTOF.DAQ/DLD1/"
       slice: 0
+      
     dldTime:
-      axis: 1
       format: per_electron
       group_name: "/uncategorised/FLASH.EXP/HEXTOF.DAQ/DLD1/"
       slice: 3
+      
+    # The auxillary channel has a special structure where the group further contains
+    # a multidim structure so further aliases are defined below
     dldAux:
-      axis: 1
       format: per_pulse
       group_name: "/uncategorised/FLASH.EXP/HEXTOF.DAQ/DLD1/"
       slice: 4
@@ -41,6 +68,9 @@ dataframe:
         cryoTemperature: 4
         sampleTemperature: 5
         dldTimeBinSize: 15
+        
+  # The prefixes of the stream names for different DAQ systems for parsing filenames
+  # (Not to be changed by user)
   stream_name_prefixes:
     pbd: "GMD_DATA_gmd_data"
     pbd2: "FL2PhotDiag_pbd2_gmd_data"
@@ -49,7 +79,20 @@ dataframe:
     fl1user3: "FLASH1_USER3_stream_2"
     fl2user1: "FLASH2_USER1_stream_2"
     fl2user2: "FLASH2_USER2_stream_2"
+
+  # The beamtime directories for different DAQ systems.
+  # (Not to be changed by user)
   beamtime_dir:
     pg2: "/asap3/flash/gpfs/pg2/"
-    hextof: "/asap3/flash/gpfs/pg2/"
-    wespe: "/asap3/fs-flash-o/gpfs/wespe/"
+
+# metadata collection from scicat
+# metadata:
+#   scicat_url: <URL>
+#   scicat_username: <username>
+#   scicat_password: <password>
+
+# The nexus collection routine shall be finalized soon for both instruments
+# nexus:
+#   reader: "flash"
+#   definition: "NXmpes"
+#   input_files: ["NXmpes_config_HEXTOF_light.json"]
diff --git a/tests/data/loader/mpes/config.yaml b/tests/data/loader/mpes/config.yaml
index e901b4d346c683f1f85b9872e791c6b04944a637..877c531afe7f9a090b78c0c87611b5deb2d82881 100644
--- a/tests/data/loader/mpes/config.yaml
+++ b/tests/data/loader/mpes/config.yaml
@@ -1 +1,3 @@
-test:
+core:
+  paths:
+    data_raw_dir: "tests/data/loader/mpes/"
diff --git a/tests/helpers.py b/tests/helpers.py
index 8384e1ce50957ee39827ade8a431e833163085df..c7f6d3e3dc9a268cbe67b86b30477d0e90ade2d7 100644
--- a/tests/helpers.py
+++ b/tests/helpers.py
@@ -7,7 +7,7 @@ import xarray as xr
 def simulate_binned_data(shape: tuple, dims: list) -> xr.DataArray:
     """Generate a fake xr.DataArray as those generated by binning.
 
-    Used for testing purpouses.
+    Used for testing purposes.
 
     Args:
         shape (tuple): Shape ouf the data
diff --git a/tests/loader/test_loaders.py b/tests/loader/test_loaders.py
index b3a97c7919700cacf0df387c32ab7eae316f6d12..1d7440883ed673d1f50e73c6ea3dcc409493f0ae 100644
--- a/tests/loader/test_loaders.py
+++ b/tests/loader/test_loaders.py
@@ -1,6 +1,9 @@
-"""Test cases for loaders used to load dataframes"""
+"""Test cases for loaders used to load dataframes
+"""
 import os
 from importlib.util import find_spec
+from pathlib import Path
+from typing import cast
 from typing import List
 
 import dask.dataframe as ddf
@@ -9,6 +12,7 @@ from _pytest.mark.structures import ParameterSet
 
 from sed.core.config import parse_config
 from sed.loader.base.loader import BaseLoader
+from sed.loader.flash.loader import FlashLoader
 from sed.loader.loader_interface import get_loader
 from sed.loader.loader_interface import get_names_of_all_loaders
 from sed.loader.utils import gather_files
@@ -18,7 +22,7 @@ package_dir = os.path.dirname(find_spec("sed").origin)
 test_data_dir = os.path.join(package_dir, "..", "tests", "data")
 
 read_types = ["one_file", "files", "one_folder", "folders", "one_run", "runs"]
-runs = {"generic": None, "mpes": None, "flash": ["43878", "43878"]}
+runs = {"generic": None, "mpes": ["30", "50"], "flash": ["43878", "43878"]}
 
 
 def get_loader_name_from_loader_object(loader: BaseLoader) -> str:
@@ -71,7 +75,7 @@ def get_all_loaders() -> List[ParameterSet]:
 
 
 @pytest.mark.parametrize("loader", get_all_loaders())
-def test_if_loaders_are_children_of_base_loader(loader):
+def test_if_loaders_are_children_of_base_loader(loader: BaseLoader):
     """Test to verify that all loaders are children of BaseLoader"""
     if loader.__name__ != "BaseLoader":
         assert isinstance(loader, BaseLoader)
@@ -79,7 +83,7 @@ def test_if_loaders_are_children_of_base_loader(loader):
 
 @pytest.mark.parametrize("loader", get_all_loaders())
 @pytest.mark.parametrize("read_type", read_types)
-def test_has_correct_read_dataframe_func(loader, read_type):
+def test_has_correct_read_dataframe_func(loader: BaseLoader, read_type: str):
     """Test if all loaders have a valid read function implemented"""
     assert callable(loader.read_dataframe)
     if loader.__name__ != "BaseLoader":
@@ -147,6 +151,62 @@ def test_has_correct_read_dataframe_func(loader, read_type):
             assert loaded_dataframe.npartitions == expected_size
             assert isinstance(loaded_metadata, dict)
 
+    if loader.__name__ == "flash":
+        loader = cast(FlashLoader, loader)
+        _, parquet_data_dir = loader.initialize_paths()
+        for file in os.listdir(Path(parquet_data_dir, "buffer")):
+            os.remove(Path(parquet_data_dir, "buffer", file))
+
+
+@pytest.mark.parametrize("loader", get_all_loaders())
+def test_get_count_rate(loader: BaseLoader):
+    """Test the get_count_rate function
+
+    Args:
+        loader (BaseLoader): the loader object to test
+    """
+    if loader.__name__ != "BaseLoader":
+        loader_name = get_loader_name_from_loader_object(loader)
+        input_folder = os.path.join(test_data_dir, "loader", loader_name)
+        for supported_file_type in loader.supported_file_types:
+            loader.read_dataframe(
+                folders=input_folder,
+                ftype=supported_file_type,
+                collect_metadata=False,
+            )
+            loaded_time, loaded_countrate = loader.get_count_rate()
+            if loaded_time is None and loaded_countrate is None:
+                pytest.skip("Not implemented")
+            assert len(loaded_time) == len(loaded_countrate)
+            loaded_time2, loaded_countrate2 = loader.get_count_rate(fids=[0])
+            assert len(loaded_time2) == len(loaded_countrate2)
+            assert len(loaded_time2) < len(loaded_time)
+
+
+@pytest.mark.parametrize("loader", get_all_loaders())
+def test_get_elapsed_time(loader: BaseLoader):
+    """Test the get_elapsed_time function
+
+    Args:
+        loader (BaseLoader): the loader object to test
+    """
+    if loader.__name__ != "BaseLoader":
+        loader_name = get_loader_name_from_loader_object(loader)
+        input_folder = os.path.join(test_data_dir, "loader", loader_name)
+        for supported_file_type in loader.supported_file_types:
+            loader.read_dataframe(
+                folders=input_folder,
+                ftype=supported_file_type,
+                collect_metadata=False,
+            )
+            elapsed_time = loader.get_elapsed_time()
+            if elapsed_time is None:
+                pytest.skip("Not implemented")
+            assert elapsed_time > 0
+            elapsed_time2 = loader.get_elapsed_time(fids=[0])
+            assert elapsed_time2 > 0
+            assert elapsed_time > elapsed_time2
+
 
 def test_mpes_timestamps():
     """Function to test if the timestamps are loaded correctly"""
diff --git a/tests/test_binning.py b/tests/test_binning.py
index e1ffe4ae39e15aae5ea40eac86261a62d33c2398..871be1bc65141c20d4422edd595effaddc42a3b8 100644
--- a/tests/test_binning.py
+++ b/tests/test_binning.py
@@ -6,10 +6,14 @@ from typing import Sequence
 from typing import Tuple
 from typing import Union
 
+import dask.dataframe as ddf
 import numpy as np
 import pandas as pd
 import pytest
+import xarray as xr
 
+from sed.binning.binning import bin_dataframe
+from sed.binning.binning import bin_partition
 from sed.binning.binning import numba_histogramdd
 from sed.binning.binning import simplify_binning_arguments
 from sed.binning.numba_bin import _hist_from_bin_range
@@ -21,7 +25,8 @@ from .helpers import get_linear_bin_edges  # noreorder
 
 sample = np.random.randn(int(1e5), 3)
 columns = ["x", "y", "z"]
-sample_df = pd.DataFrame(sample, columns=columns)
+sample_pdf = pd.DataFrame(sample, columns=columns)
+sample_ddf = ddf.from_pandas(sample_pdf, npartitions=10)
 bins: Sequence[int] = tuple(np.random.randint(5, 50, size=3, dtype=int))
 ranges_array = 0.5 + np.random.rand(6).reshape(3, 2)
 ranges_array[:, 0] = -ranges_array[:, 0]
@@ -290,17 +295,12 @@ def test_bin_edges_to_bin_centers():
         assert stepped_array[i + 1] > bin_centers[i]
 
 
-bins = [10, 20, 30]
-axes = ["a", "b", "c"]
-ranges = [(-1, 1), (-2, 2), (-3, 3)]
-
-
 @pytest.mark.parametrize(
     "args",
     [
-        (bins[:1], axes[:1], ranges[:1], 1),
-        (bins[:2], axes[:2], ranges[:2], 2),
-        (bins[:3], axes[:3], ranges[:3], 3),
+        (bins[:1], columns[:1], ranges[:1], 1),
+        (bins[:2], columns[:2], ranges[:2], 2),
+        (bins[:3], columns[:3], ranges[:3], 3),
     ],
     ids=lambda x: f"ndim: {x[3]}",
 )
@@ -398,3 +398,109 @@ def test_simplify_binning_arguments(
         np.testing.assert_array_equal(axes__[i], axes_expected[i])
         if ranges__ is not None:
             np.testing.assert_array_equal(ranges__[i], ranges_expected[i])
+
+
+def test_bin_partition():
+    """Test bin_partition function"""
+    # test skipping checks in bin_partition
+    with pytest.raises(TypeError):
+        res = bin_partition(
+            part=sample_pdf,
+            bins=[10, np.array([10, 20])],
+            axes=columns,
+            ranges=ranges,
+            skip_test=True,
+        )
+    with pytest.raises(TypeError):
+        res = bin_partition(
+            part=sample_pdf,
+            bins=bins,
+            axes=["x", 10],
+            ranges=ranges,
+            skip_test=True,
+        )
+    # binning with bin numbers
+    res1, edges1 = bin_partition(
+        part=sample_pdf,
+        bins=bins,
+        axes=columns,
+        ranges=ranges,
+        skip_test=False,
+        return_edges=True,
+    )
+    assert isinstance(res1, np.ndarray)
+    # binning with bin centers
+    bin_centers = [np.linspace(r[0], r[1], b, endpoint=False) for r, b in zip(ranges, bins)]
+
+    res2, edges2 = bin_partition(
+        part=sample_pdf,
+        bins=bin_centers,
+        axes=columns,
+        skip_test=False,
+        return_edges=True,
+    )
+    assert isinstance(res2, np.ndarray)
+    np.testing.assert_allclose(res1, res2)
+    for edge1, edge2 in zip(edges1, edges2):
+        np.testing.assert_allclose(edge1, edge2)
+
+    # test jittering, list
+    res = bin_partition(
+        part=sample_pdf,
+        bins=bins,
+        axes=columns,
+        ranges=ranges,
+        skip_test=False,
+        jitter=columns,
+    )
+    assert not np.allclose(res, res1)
+    # test jittering, dict
+    res = bin_partition(
+        part=sample_pdf,
+        bins=bins,
+        axes=columns,
+        ranges=ranges,
+        skip_test=False,
+        jitter={axis: {"amplitude": 0.5, "mode": "normal"} for axis in columns},
+    )
+    assert not np.allclose(res, res1)
+    # numpy mode
+    with pytest.raises(ValueError):
+        res = bin_partition(
+            part=sample_pdf,
+            bins=bins,
+            axes=columns,
+            ranges=ranges,
+            skip_test=False,
+            hist_mode="invalid",
+        )
+    res = bin_partition(
+        part=sample_pdf,
+        bins=bins,
+        axes=columns,
+        ranges=ranges,
+        skip_test=False,
+        hist_mode="numpy",
+    )
+    assert np.allclose(res, res1)
+
+
+def test_bin_dataframe():
+    """Test bin_dataframe function"""
+    res = bin_dataframe(df=sample_ddf, bins=bins, axes=columns, ranges=ranges)
+    assert isinstance(res, xr.DataArray)
+    for i, axis in enumerate(columns):
+        assert len(res.coords[axis]) == bins[i]
+    # binning with bin centers
+    bin_centers = [np.linspace(r[0], r[1], b, endpoint=False) for r, b in zip(ranges, bins)]
+    res = bin_dataframe(df=sample_ddf, bins=bin_centers, axes=columns)
+    assert isinstance(res, xr.DataArray)
+    for i, axis in enumerate(columns):
+        assert len(res.coords[axis]) == len(bin_centers[i])
+    # legacy modes
+    with pytest.raises(ValueError):
+        res = bin_dataframe(df=sample_ddf, bins=bins, axes=columns, ranges=ranges, mode="invalid")
+    res2 = bin_dataframe(df=sample_ddf, bins=bins, axes=columns, ranges=ranges, mode="legacy")
+    np.testing.assert_allclose(res.values, res2.values)
+    res2 = bin_dataframe(df=sample_ddf, bins=bins, axes=columns, ranges=ranges, mode="lean")
+    np.testing.assert_allclose(res.values, res2.values)
diff --git a/tests/test_config.py b/tests/test_config.py
index 9767fdaf0c69283a5f06328a253342493edc2fc3..cee914dee339d5dfa0d7072a6d5cfab029d910fa 100644
--- a/tests/test_config.py
+++ b/tests/test_config.py
@@ -1,5 +1,4 @@
 """This is a code that performs several tests for the settings loader.
-
 """
 import os
 import tempfile
diff --git a/tests/test_diagnostics.py b/tests/test_diagnostics.py
index 929ec0dac0a641ea530f142072866f7e1eb3d249..ab80bde7410200f6a8dbd8cbd33779fae662760e 100644
--- a/tests/test_diagnostics.py
+++ b/tests/test_diagnostics.py
@@ -1,4 +1,42 @@
-# import random
-# import numpy as np
-# import pytest
-# import xarray as xr
+"""Module tests.diagnostics, tests for the sed.diagnostics module
+"""
+import glob
+import itertools
+import os
+from importlib.util import find_spec
+
+import pytest
+
+from sed.core.config import parse_config
+from sed.diagnostics import grid_histogram
+from sed.loader.loader_interface import get_loader
+
+#  pylint: disable=duplicate-code
+package_dir = os.path.dirname(find_spec("sed").origin)
+df_folder = package_dir + "/../tests/data/loader/mpes/"
+folder = package_dir + "/../tests/data/calibrator/"
+files = glob.glob(df_folder + "*.h5")
+config = parse_config(package_dir + "/../tests/data/config/config.yaml")
+loader = get_loader("mpes", config=config)
+
+
+@pytest.mark.parametrize(
+    "ncols, backend",
+    itertools.product([1, 2, 3, 4], ["matplotlib", "bokeh"]),
+)
+def test_plot_histogram(ncols: int, backend: str):
+    """Test generation of data histogram
+
+    Args:
+        ncols (int): number of columns
+        backend (str): plotting backend to use
+    """
+    dataframe, _ = loader.read_dataframe(files=files)
+    axes = config["histogram"]["axes"]
+    ranges = config["histogram"]["ranges"]
+    bins = config["histogram"]["bins"]
+    for loc, axis in enumerate(axes):
+        if axis.startswith("@"):
+            axes[loc] = config["dataframe"].get(axis.strip("@"))
+    values = {axis: dataframe[axis].compute() for axis in axes}
+    grid_histogram(values, ncols, axes, bins, ranges, backend)
diff --git a/tests/test_processor.py b/tests/test_processor.py
new file mode 100644
index 0000000000000000000000000000000000000000..fba83d779b814b72ee0739ea9efb47d516c1b853
--- /dev/null
+++ b/tests/test_processor.py
@@ -0,0 +1,695 @@
+"""Module tests.processor, tests for the sed.core.processor module
+"""
+import csv
+import glob
+import itertools
+import os
+import tempfile
+from importlib.util import find_spec
+from pathlib import Path
+from typing import Any
+from typing import Dict
+from typing import List
+from typing import Tuple
+
+import dask.dataframe as ddf
+import numpy as np
+import pytest
+
+from sed import SedProcessor
+from sed.core.config import parse_config
+from sed.loader.loader_interface import get_loader
+
+#  pylint: disable=duplicate-code
+package_dir = os.path.dirname(find_spec("sed").origin)
+df_folder = package_dir + "/../tests/data/loader/generic/"
+folder = package_dir + "/../tests/data/calibrator/"
+files = glob.glob(df_folder + "*.parquet")
+runs = ["43878", "43878"]
+loader = get_loader(loader_name="generic")
+source_folder = package_dir + "/../"
+dest_folder = tempfile.mkdtemp()
+gid = os.getgid()
+
+traces_list = []
+with open(folder + "traces.csv", newline="", encoding="utf-8") as csvfile:
+    reader = csv.reader(csvfile, quoting=csv.QUOTE_NONNUMERIC)
+    for row in reader:
+        traces_list.append(row)
+traces = np.asarray(traces_list).T
+with open(folder + "tof.csv", newline="", encoding="utf-8") as csvfile:
+    reader = csv.reader(csvfile, quoting=csv.QUOTE_NONNUMERIC)
+    tof = np.asarray(next(reader))
+with open(folder + "biases.csv", newline="", encoding="utf-8") as csvfile:
+    reader = csv.reader(csvfile, quoting=csv.QUOTE_NONNUMERIC)
+    biases = np.asarray(next(reader))
+
+
+def test_processor_from_dataframe():
+    """Test generation of the processor from a dataframe object"""
+    config = {"core": {"loader": "generic"}}
+    dataframe, _ = loader.read_dataframe(files=files)
+    processor = SedProcessor(
+        dataframe=dataframe,
+        config=config,
+        folder_config={},
+        user_config={},
+        system_config={},
+    )
+    for column in dataframe.columns:
+        assert (dataframe[column].compute() == processor.dataframe[column].compute()).all()
+
+
+def test_processor_from_files():
+    """Test generation of the processor from a list of files"""
+    config = {"core": {"loader": "generic"}}
+    dataframe, _ = loader.read_dataframe(files=files)
+    processor = SedProcessor(
+        files=files,
+        config=config,
+        folder_config={},
+        user_config={},
+        system_config={},
+    )
+    for column in dataframe.columns:
+        assert (dataframe[column].compute() == processor.dataframe[column].compute()).all()
+
+
+def test_processor_from_folders():
+    """Test generation of the processor from a folder"""
+    config = {"core": {"loader": "generic"}}
+    dataframe, _ = loader.read_dataframe(files=files)
+    processor = SedProcessor(
+        folder=df_folder,
+        config=config,
+        folder_config={},
+        user_config={},
+        system_config={},
+    )
+    for column in dataframe.columns:
+        assert (dataframe[column].compute() == processor.dataframe[column].compute()).all()
+
+
+def test_processor_from_runs():
+    """Test generation of the processor from runs"""
+    config = df_folder + "../flash/config.yaml"
+    processor = SedProcessor(
+        folder=df_folder + "../flash/",
+        config=config,
+        runs=runs,
+        folder_config={},
+        user_config={},
+        system_config={},
+    )
+    assert "dldPosX" in processor.dataframe.columns
+    # cleanup flash inermediaries
+    _, parquet_data_dir = processor.loader.initialize_paths()
+    for file in os.listdir(Path(parquet_data_dir, "buffer")):
+        os.remove(Path(parquet_data_dir, "buffer", file))
+
+
+def test_additional_parameter_to_loader():
+    """Test if additinal keyword parameter can be passed to the loader from the
+    Processor initialiuzation.
+    """
+    config = {"core": {"loader": "generic"}}
+    processor = SedProcessor(
+        folder=df_folder,
+        ftype="json",
+        config=config,
+        folder_config={},
+        user_config={},
+        system_config={},
+    )
+    assert processor.files[0].find("json") > -1
+
+
+def test_repr():
+    """test the ___repr___ method"""
+    config = {"core": {"loader": "generic"}}
+    processor = SedProcessor(
+        config=config,
+        folder_config={},
+        user_config={},
+        system_config={},
+    )
+    processor_str = str(processor)
+    assert processor_str.find("No Data loaded") > 0
+    with pytest.raises(ValueError):
+        processor.load()
+    processor.load(files=files, metadata={"test": {"key1": "value1"}})
+    processor_str = str(processor)
+    assert processor_str.find("ADC") > 0
+    assert processor_str.find("key1") > 0
+
+
+def test_attributes_setters():
+    """Test class attributes and setters."""
+    config = {"core": {"loader": "generic"}}
+    processor = SedProcessor(
+        config=config,
+        folder_config={},
+        user_config={},
+        system_config={},
+    )
+    processor.load(files=files, metadata={"test": {"key1": "value1"}})
+    dataframe = processor.dataframe
+    assert isinstance(dataframe, ddf.DataFrame)
+    with pytest.raises(ValueError):
+        processor.dataframe = dataframe["X"]
+    dataframe["X"] = dataframe["Y"]
+    processor.dataframe = dataframe
+    np.testing.assert_allclose(
+        processor.dataframe["X"].compute(),
+        processor.dataframe["Y"].compute(),
+    )
+    processor_metadata = processor.attributes
+    assert isinstance(processor_metadata, dict)
+    assert "test" in processor_metadata.keys()
+    processor.add_attribute({"key2": 5}, name="test2")
+    assert processor.attributes["test2"]["key2"] == 5
+    assert processor.config["core"]["loader"] == "generic"
+    assert len(processor.files) == 2
+
+
+def test_copy_tool():
+    """Test the copy tool functionality in the processor"""
+    config = {"core": {"loader": "generic", "use_copy_tool": True}}
+    processor = SedProcessor(
+        config=config,
+        folder_config={},
+        user_config={},
+        system_config={},
+    )
+    assert processor.use_copy_tool is False
+    config = {
+        "core": {
+            "loader": "generic",
+            "use_copy_tool": True,
+            "copy_tool_source": source_folder,
+            "copy_tool_dest": dest_folder,
+            "copy_tool_kwds": {"gid": os.getgid()},
+        },
+    }
+    processor = SedProcessor(
+        config=config,
+        folder_config={},
+        user_config={},
+        system_config={},
+    )
+    assert processor.use_copy_tool is True
+    processor.load(files=files)
+    assert processor.files[0].find(dest_folder) > -1
+
+
+feature4 = np.array([[203.2, 341.96], [299.16, 345.32], [304.38, 149.88], [199.52, 152.48]])
+feature5 = np.array(
+    [[203.2, 341.96], [299.16, 345.32], [304.38, 149.88], [199.52, 152.48], [248.29, 248.62]],
+)
+feature6 = np.array(
+    [
+        [203.2, 341.96],
+        [299.16, 345.32],
+        [350.25, 243.70],
+        [304.38, 149.88],
+        [199.52, 152.48],
+        [154.28, 242.27],
+    ],
+)
+feature7 = np.array(
+    [
+        [203.2, 341.96],
+        [299.16, 345.32],
+        [350.25, 243.70],
+        [304.38, 149.88],
+        [199.52, 152.48],
+        [154.28, 242.27],
+        [248.29, 248.62],
+    ],
+)
+feature_list = [feature4, feature5, feature6, feature7]
+
+adjust_params = {
+    "scale": np.random.randint(1, 10) / 10 + 0.5,
+    "xtrans": np.random.randint(1, 50),
+    "ytrans": np.random.randint(1, 50),
+    "angle": np.random.randint(1, 50),
+}
+
+
+@pytest.mark.parametrize(
+    "features",
+    feature_list,
+)
+def test_momentum_correction_workflow(features: np.ndarray):
+    """Test for the momentum correction workflow"""
+    config = parse_config(
+        config={"core": {"loader": "generic"}},
+        folder_config={},
+        user_config={},
+        system_config={},
+    )
+    processor = SedProcessor(
+        folder=df_folder,
+        config=config,
+        folder_config={},
+        user_config={},
+        system_config={},
+    )
+    processor.bin_and_load_momentum_calibration(apply=True)
+    assert processor.mc.slice is not None
+    assert processor.mc.pouter is None
+    if len(features) == 5 or len(features) == 7:
+        rotsym = len(features) - 1
+        include_center = True
+    else:
+        rotsym = len(features)
+        include_center = False
+    processor.define_features(
+        features=features,
+        rotation_symmetry=rotsym,
+        include_center=include_center,
+        apply=True,
+    )
+    assert len(processor.mc.pouter_ord) == rotsym
+    processor.generate_splinewarp(use_center=include_center)
+    processor.save_splinewarp(filename=f"sed_config{len(features)}.yaml", overwrite=True)
+    pouter_ord = processor.mc.pouter_ord
+    cdeform_field = processor.mc.cdeform_field
+    rdeform_field = processor.mc.rdeform_field
+    # load features
+    processor = SedProcessor(
+        folder=df_folder,
+        config=config,
+        folder_config=f"sed_config{len(features)}.yaml",
+        user_config={},
+        system_config={},
+    )
+    processor.generate_splinewarp()
+    assert len(processor.mc.pouter_ord) == rotsym
+    np.testing.assert_allclose(processor.mc.pouter_ord, pouter_ord)
+    np.testing.assert_allclose(processor.mc.cdeform_field, cdeform_field)
+    np.testing.assert_allclose(processor.mc.rdeform_field, rdeform_field)
+    os.remove(f"sed_config{len(features)}.yaml")
+
+
+def test_pose_adjustment():
+    """Test for the pose correction and application of momentum correction workflow"""
+    config = parse_config(
+        config={"core": {"loader": "generic"}},
+        folder_config={},
+        user_config={},
+        system_config={},
+    )
+    processor = SedProcessor(
+        folder=df_folder,
+        config=config,
+        folder_config={},
+        user_config={},
+        system_config={},
+    )
+    with pytest.raises(ValueError):
+        processor.pose_adjustment(**adjust_params, use_correction=False, apply=True)
+
+    processor.bin_and_load_momentum_calibration(apply=True)
+    # test pose adjustment
+    processor.pose_adjustment(**adjust_params, use_correction=False, apply=True)
+
+    processor = SedProcessor(
+        folder=df_folder,
+        config=config,
+        folder_config={},
+        user_config={},
+        system_config={},
+    )
+    with pytest.raises(ValueError):
+        processor.apply_momentum_correction()
+    processor.bin_and_load_momentum_calibration(apply=True)
+    processor.define_features(
+        features=feature7,
+        rotation_symmetry=6,
+        include_center=True,
+        apply=True,
+    )
+    processor.generate_splinewarp(use_center=True)
+    processor.pose_adjustment(**adjust_params, apply=True)
+    processor.apply_momentum_correction()
+    assert "Xm" in processor.dataframe.columns
+    assert "Ym" in processor.dataframe.columns
+
+
+point_a = [308, 345]
+k_distance = 4 / 3 * np.pi / 3.28
+k_coord_a = [k_distance * 0.3, k_distance * 0.8]
+
+
+def test_momentum_calibration_workflow():
+    """Test the calibration of the momentum axes"""
+    config = parse_config(
+        config={"core": {"loader": "generic"}},
+        folder_config={},
+        user_config={},
+        system_config={},
+    )
+    processor = SedProcessor(
+        folder=df_folder,
+        config=config,
+        folder_config={},
+        user_config={},
+        system_config={},
+    )
+    with pytest.raises(ValueError):
+        processor.apply_momentum_calibration()
+    with pytest.raises(ValueError):
+        processor.calibrate_momentum_axes(apply=True)
+    processor.bin_and_load_momentum_calibration(apply=True)
+    with pytest.raises(AssertionError):
+        processor.calibrate_momentum_axes(point_a=point_a, apply=True)
+    processor.calibrate_momentum_axes(point_a=point_a, k_distance=k_distance, apply=True)
+    assert processor.mc.calibration["kx_scale"] == processor.mc.calibration["ky_scale"]
+    with pytest.raises(AssertionError):
+        processor.calibrate_momentum_axes(point_a=point_a, k_coord_a=k_coord_a, apply=True)
+    with pytest.raises(AssertionError):
+        processor.calibrate_momentum_axes(point_a=point_a, equiscale=False, apply=True)
+    processor.calibrate_momentum_axes(
+        point_a=point_a,
+        k_coord_a=k_coord_a,
+        equiscale=False,
+        apply=True,
+    )
+    assert processor.mc.calibration["kx_scale"] != processor.mc.calibration["ky_scale"]
+    processor.save_momentum_calibration()
+    processor = SedProcessor(
+        folder=df_folder,
+        config=config,
+        user_config={},
+        system_config={},
+    )
+    processor.apply_momentum_calibration()
+    assert (
+        processor.config["momentum"]["calibration"]["kx_scale"]
+        != processor.config["momentum"]["calibration"]["ky_scale"]
+    )
+    assert "kx" in processor.dataframe.columns
+    assert "ky" in processor.dataframe.columns
+    os.remove("sed_config.yaml")
+
+
+def test_energy_correction():
+    """Test energy correction workflow."""
+    config = parse_config(
+        config={"core": {"loader": "generic"}},
+        folder_config={},
+        user_config={},
+        system_config={},
+    )
+    processor = SedProcessor(
+        folder=df_folder,
+        config=config,
+        folder_config={},
+        user_config={},
+        system_config={},
+    )
+    with pytest.raises(ValueError):
+        processor.apply_energy_correction()
+    with pytest.raises(ValueError):
+        processor.adjust_energy_correction(apply=True)
+    assert processor._pre_binned is not None  # pylint: disable=protected-access
+    processor.adjust_energy_correction(
+        correction_type="Lorentzian",
+        amplitude=2.5,
+        center=(730, 730),
+        gamma=920,
+        apply=True,
+    )
+    assert processor.ec.correction["correction_type"] == "Lorentzian"
+    processor.save_energy_correction()
+    processor = SedProcessor(
+        folder=df_folder,
+        config=config,
+        user_config={},
+        system_config={},
+    )
+    processor.adjust_energy_correction(apply=True)
+    assert processor.ec.correction["correction_type"] == "Lorentzian"
+    processor = SedProcessor(
+        folder=df_folder,
+        config=config,
+        user_config={},
+        system_config={},
+    )
+    processor.apply_energy_correction()
+    assert "tm" in processor.dataframe.columns
+    os.remove("sed_config.yaml")
+
+
+energy_scales = ["kinetic", "binding"]
+calibration_methods = ["lmfit", "lstsq", "lsqr"]
+
+
+@pytest.mark.parametrize(
+    "energy_scale, calibration_method",
+    itertools.product(energy_scales, calibration_methods),
+)
+def test_energy_calibration_workflow(energy_scale: str, calibration_method: str):
+    """Test energy calibration workflow
+
+    Args:
+        energy_scale (str): Energy scale
+        calibration_method (str): _description_
+    """
+    config = parse_config(
+        config={"core": {"loader": "mpes"}},
+        folder_config={},
+        user_config={},
+        system_config={},
+    )
+    processor = SedProcessor(
+        folder=df_folder + "../mpes/",
+        config=config,
+        folder_config={},
+        user_config={},
+        system_config={},
+    )
+    with pytest.raises(ValueError):
+        processor.load_bias_series(data_files=glob.glob(df_folder + "../mpes/*.h5"), normalize=True)
+    processor.load_bias_series(
+        data_files=glob.glob(df_folder + "../mpes/*.h5"),
+        normalize=True,
+        bias_key="@KTOF:Lens:Sample:V",
+    )
+    assert len(processor.ec.biases) == 2
+    # load test data into class
+    processor.ec.load_data(biases=biases, traces=traces, tof=tof)
+    processor.ec.normalize()
+    ref_id = 5
+    rng = (66100, 67000)
+    processor.find_bias_peaks(ranges=rng, ref_id=ref_id, infer_others=True, apply=True)
+    ranges: List[Tuple[Any, ...]] = [
+        (64638.0, 65386.0),
+        (64913.0, 65683.0),
+        (65188.0, 65991.0),
+        (65474.0, 66310.0),
+        (65782.0, 66651.0),
+        (66101.0, 67003.0),
+        (66442.0, 67388.0),
+        (66794.0, 67795.0),
+        (67190.0, 68213.0),
+        (67575.0, 68664.0),
+        (67993.0, 69148.0),
+    ]
+    processor.find_bias_peaks(ranges=ranges, infer_others=False, apply=True)
+    np.testing.assert_allclose(processor.ec.featranges, ranges)
+    ref_id = 4
+    ref_energy = -0.5
+    with pytest.raises(ValueError):
+        processor.calibrate_energy_axis(
+            ref_energy=ref_energy,
+            ref_id=ref_id,
+            energy_scale="myfantasyscale",
+        )
+    with pytest.raises(NotImplementedError):
+        processor.calibrate_energy_axis(
+            ref_energy=ref_energy,
+            ref_id=ref_id,
+            method="myfantasymethod",
+        )
+    processor.calibrate_energy_axis(
+        ref_energy=ref_energy,
+        ref_id=ref_id,
+        energy_scale=energy_scale,
+        method=calibration_method,
+    )
+    assert processor.ec.calibration["energy_scale"] == energy_scale
+    processor.save_energy_calibration()
+    processor.append_energy_axis()
+    assert "energy" in processor.dataframe.columns
+    processor = SedProcessor(
+        folder=df_folder + "../mpes/",
+        config=config,
+        user_config={},
+        system_config={},
+    )
+    processor.append_energy_axis(preview=True)
+    assert "energy" in processor.dataframe.columns
+    assert processor.attributes["energy_calibration"]["calibration"]["energy_scale"] == energy_scale
+    os.remove("sed_config.yaml")
+
+
+def test_delay_calibration_workflow():
+    """Test the delay calibration workflow"""
+    config = parse_config(
+        config={"core": {"loader": "mpes"}},
+        folder_config={},
+        user_config={},
+        system_config={},
+    )
+    processor = SedProcessor(
+        folder=df_folder + "../mpes/",
+        config=config,
+        folder_config={},
+        user_config={},
+        system_config={},
+    )
+    delay_range = (-500, 1500)
+    processor.calibrate_delay_axis(delay_range=delay_range, preview=False)
+    # read from datafile
+    with pytest.raises(NotImplementedError):
+        processor.calibrate_delay_axis(preview=True)
+    processor.calibrate_delay_axis(
+        p1_key="@trARPES:DelayStage:p1",
+        p2_key="@trARPES:DelayStage:p2",
+        t0_key="@trARPES:DelayStage:t0",
+        preview=True,
+    )
+    assert "delay" in processor.dataframe.columns
+
+
+def test_add_jitter():
+    """Test the jittering function"""
+    config = parse_config(
+        config={"core": {"loader": "generic"}},
+        folder_config={},
+        user_config={},
+        system_config={},
+    )
+    processor = SedProcessor(
+        folder=df_folder,
+        config=config,
+        folder_config={},
+        user_config={},
+        system_config={},
+    )
+    res1 = processor.dataframe["X"].compute()
+    processor.add_jitter()
+    res2 = processor.dataframe["X"].compute()
+    np.testing.assert_allclose(res1, np.round(res1))
+    np.testing.assert_allclose(res1, np.round(res2))
+    assert (res1 != res2).all()
+
+
+def test_event_histogram():
+    """Test histogram plotting function"""
+    config = parse_config(
+        config={"core": {"loader": "generic"}},
+        folder_config={},
+        user_config={},
+        system_config={},
+    )
+    processor = SedProcessor(
+        folder=df_folder,
+        config=config,
+        folder_config={},
+        user_config={},
+        system_config={},
+    )
+    processor.view_event_histogram(dfpid=0)
+    with pytest.raises(ValueError):
+        processor.view_event_histogram(dfpid=5)
+
+
+def test_compute():
+    """Test binning of final result"""
+    config = parse_config(
+        config={"core": {"loader": "generic"}},
+        folder_config={},
+        user_config={},
+        system_config={},
+    )
+    processor = SedProcessor(
+        folder=df_folder,
+        config=config,
+        folder_config={},
+        user_config={},
+        system_config={},
+    )
+    bins = [10, 10, 10, 10]
+    axes = ["X", "Y", "t", "ADC"]
+    ranges = [[0, 2048], [0, 2048], [0, 200000], [0, 50000]]
+    result = processor.compute(bins=bins, axes=axes, ranges=ranges, df_partitions=5)
+    assert result.data.shape == (10, 10, 10, 10)
+    assert result.data.sum(axis=(0, 1, 2, 3)) > 0
+
+
+metadata: Dict[Any, Any] = {}
+metadata["entry_title"] = "Title"
+# User
+metadata["user0"] = {}
+metadata["user0"]["name"] = "Name"
+metadata["user0"]["email"] = "email"
+# NXinstrument
+metadata["instrument"] = {}
+# analyzer
+metadata["instrument"]["analyzer"] = {}
+metadata["instrument"]["analyzer"]["energy_resolution"] = 110.0
+metadata["instrument"]["analyzer"]["momentum_resolution"] = 0.08
+# probe beam
+metadata["instrument"]["beam"] = {}
+metadata["instrument"]["beam"]["probe"] = {}
+metadata["instrument"]["beam"]["probe"]["incident_energy"] = 21.7
+# sample
+metadata["sample"] = {}
+metadata["sample"]["preparation_date"] = "2019-01-13T10:00:00+00:00"
+metadata["sample"]["name"] = "Sample Name"
+
+
+def test_save():
+    """Test the save functionality"""
+    config = parse_config(
+        config={"dataframe": {"tof_binning": 1}},
+        folder_config={},
+        user_config=package_dir + "/../sed/config/mpes_example_config.yaml",
+        system_config={},
+    )
+    processor = SedProcessor(
+        folder=df_folder + "../mpes/",
+        config=config,
+        folder_config={},
+        user_config={},
+        system_config={},
+        metadata=metadata,
+        collect_metadata=True,
+    )
+    processor.apply_momentum_calibration()
+    processor.append_energy_axis()
+    processor.calibrate_delay_axis()
+    with pytest.raises(NameError):
+        processor.save("output.tiff")
+    axes = ["kx", "ky", "energy", "delay"]
+    bins = [100, 100, 200, 50]
+    ranges = [[-2, 2], [-2, 2], [-4, 2], [-600, 1600]]
+    processor.compute(bins=bins, axes=axes, ranges=ranges)
+    with pytest.raises(NotImplementedError):
+        processor.save("output.jpeg")
+    processor.save("output.tiff")
+    assert os.path.isfile("output.tiff")
+    os.remove("output.tiff")
+    processor.save("output.h5")
+    assert os.path.isfile("output.h5")
+    os.remove("output.h5")
+    processor.save(
+        "output.nxs",
+        input_files=df_folder + "../../../../sed/config/NXmpes_config.json",
+    )
+    assert os.path.isfile("output.nxs")
+    os.remove("output.nxs")
diff --git a/tutorial/3 - Conversion Pipeline for example time-resolved ARPES data.ipynb b/tutorial/3 - Conversion Pipeline for example time-resolved ARPES data.ipynb
index 167d46a2f1aad2c8d9aca9805facadee4d9bc7d3..1363379050989036c578dab9d84a928cde5b7586 100644
--- a/tutorial/3 - Conversion Pipeline for example time-resolved ARPES data.ipynb	
+++ b/tutorial/3 - Conversion Pipeline for example time-resolved ARPES data.ipynb	
@@ -64,7 +64,7 @@
     "# The Scan directory\n",
     "fdir = data_path + '/Scan049_1'\n",
     "# create sed processor using the config file:\n",
-    "sp = sed.SedProcessor(folder=fdir, config=\"../tests/data/config/config.yaml\")"
+    "sp = sed.SedProcessor(folder=fdir, config=\"../sed/config/mpes_example_config.yaml\")"
    ]
   },
   {
@@ -447,11 +447,13 @@
    "source": [
     "# Option 1 = specify the ranges containing a common feature (e.g an equivalent peak) for all bias scans\n",
     "# rg = [(129031.03103103103, 129621.62162162163), (129541.54154154155, 130142.14214214214), (130062.06206206206, 130662.66266266267), (130612.61261261262, 131213.21321321322), (131203.20320320321, 131803.8038038038), (131793.7937937938, 132384.38438438438), (132434.43443443443, 133045.04504504506), (133105.10510510512, 133715.71571571572), (133805.8058058058, 134436.43643643643), (134546.54654654654, 135197.1971971972)]\n",
+    "# sp.find_bias_peaks(ranges=rg, infer_others=False)\n",
     "# Option 2 = specify the range for one curve and infer the others\n",
+    "# This will open an interactive tool to select the correct ranges for the curves.\n",
     "# IMPORTANT: Don't choose the range too narrow about a peak, and choose a refid \n",
     "# somewhere in the middle or towards larger biases!\n",
     "rg = (66100, 67000)\n",
-    "sp.find_bias_peaks(ranges=rg, ref_id=5, infer_others=True)"
+    "sp.find_bias_peaks(ranges=rg, ref_id=5, infer_others=True, apply=True)"
    ]
   },
   {
diff --git a/tutorial/4 - Metadata collection and export to NeXus.ipynb b/tutorial/4 - Metadata collection and export to NeXus.ipynb
index d4ec135ccae8e07e946c7046469acccdff1fb25e..f7e629ec144aa9ce71fcbdaf9b10cf005800bd69 100644
--- a/tutorial/4 - Metadata collection and export to NeXus.ipynb	
+++ b/tutorial/4 - Metadata collection and export to NeXus.ipynb	
@@ -148,7 +148,7 @@
     "# The Scan directory\n",
     "fdir = data_path + '/Scan049_1'\n",
     "# create sed processor using the config file, and collect the meta data from the files:\n",
-    "sp = sed.SedProcessor(folder=fdir, config=\"../tests/data/config/config.yaml\", metadata=metadata, collect_metadata=True)"
+    "sp = sed.SedProcessor(folder=fdir, config=\"../sed/config/mpes_example_config.yaml\", metadata=metadata, collect_metadata=True)"
    ]
   },
   {