diff --git a/.cspell/custom-dictionary.txt b/.cspell/custom-dictionary.txt
index f634cba4..5123e47d 100644
--- a/.cspell/custom-dictionary.txt
+++ b/.cspell/custom-dictionary.txt
@@ -38,6 +38,7 @@ caldir
 calib
 calibdict
 caplog
+capsys
 cdeform
 cdeformfield
 cdisp
@@ -77,6 +78,8 @@ datastreams
 datestring
 ddir
 delaxes
+delayeds
+delenv
 Desy
 Deutsches
 dfield
@@ -85,12 +88,14 @@ dfpart
 dfpid
 dictionarized
 dictmerge
+DLDAUX
 DOOCS
 dpkg
 dropna
 dset
 dsets
 dtype
+dtypes
 easimon
 ecalibdict
 electronanalyser
@@ -125,6 +130,7 @@ ftype
 fwhm
 genindex
 getgid
+getgrgid
 getmtime
 gpfs
 griddata
@@ -290,6 +296,7 @@ ptargs
 pullrequest
 pval
 pyarrow
+pydantic
 pydata
 pyenv
 pygments
@@ -330,6 +337,7 @@ scipy
 SDIAG
 sdir
 segs
+setp
 sfile
 shutil
 Sixten
@@ -340,6 +348,8 @@ splinewarp
 stackax
 stackaxis
 stepsize
+subchannel
+subchannels
 subdir
 subdirs
 subfolders
@@ -401,6 +411,8 @@ xpos
 xratio
 xrng
 xscale
+xticklabels
+xticks
 xtrans
 Xuser
 xval
@@ -411,6 +423,8 @@ ylabel
 ypos
 yratio
 yscale
+yticklabels
+yticks
 ytrans
 zain
 Zenodo
diff --git a/.github/workflows/benchmark.yml b/.github/workflows/benchmark.yml
index bd8a09fd..ddf6dfc2 100644
--- a/.github/workflows/benchmark.yml
+++ b/.github/workflows/benchmark.yml
@@ -4,9 +4,12 @@ name: benchmark
 on:
     workflow_dispatch:
     push:
-        branches: [ main, create-pull-request/patch ]
-        paths-ignore:
-          pyproject.toml
+      branches: [ main]
+    pull_request:
+      branches: [ main ]
+
+env:
+  UV_SYSTEM_PYTHON: true
 
 jobs:
   benchmark:
@@ -16,7 +19,7 @@ jobs:
       - name: Check out the repository
         uses: actions/checkout@v4
         with:
-          lfs: true
+          fetch-depth: 0
 
       - uses: tibdex/github-app-token@v1
         id: generate-token
@@ -24,20 +27,24 @@ jobs:
           app_id: ${{ secrets.APP_ID }}
           private_key: ${{ secrets.APP_PRIVATE_KEY }}
 
-      # Use cached python and dependencies, install poetry
-      - name: "Setup Python, Poetry and Dependencies"
-        uses: packetcoders/action-setup-cache-python-poetry@main
+      # Setup python
+      - name: Set up Python 3.10
+        uses: actions/setup-python@v5
         with:
-          python-version: 3.8
-          poetry-version: 1.2.2
+          python-version: "3.10"
+
+      - name: Install dependencies
+        run: |
+          curl -LsSf https://astral.sh/uv/install.sh | sh
 
-      - name: Install project dependencies
-        run: poetry install
+      - name: Install package
+        run: |
+          uv pip install ".[dev]"
 
       # Run benchmarks
-      - name: Run benchmarks on python 3.8
+      - name: Run benchmarks on python 3.10
         run: |
-          poetry run pytest --full-trace --show-capture=no -sv benchmarks/benchmark_*.py
+          pytest --full-trace --show-capture=no -sv benchmarks/benchmark_*.py
 
       - name: Obtain git status
         id: status
diff --git a/.github/workflows/documentation.yml b/.github/workflows/documentation.yml
index 17db4420..da4be209 100644
--- a/.github/workflows/documentation.yml
+++ b/.github/workflows/documentation.yml
@@ -5,12 +5,14 @@ on:
     branches: [ main ]
     tags: [ v* ]
     paths:
-      - sed/**/*
+      - src/sed/**/*
       - tutorial/**
       - .github/workflows/documentation.yml
   # Allows you to run this workflow manually from the Actions tab
   workflow_dispatch:
 
+env:
+  UV_SYSTEM_PYTHON: true
 
 jobs:
   build:
@@ -26,69 +28,50 @@ jobs:
           remove-android: 'true'
           remove-docker-images: 'true'
 
-      #  Check out repo and set up Python
-      - name: Check out the repository
-        uses: actions/checkout@v4
+      # Check out repo and set up Python
+      - uses: actions/checkout@v4
         with:
-          lfs: true
+          fetch-depth: 0
 
-      # Use cached python and dependencies, install poetry
-      - name: "Setup Python, Poetry and Dependencies"
-        uses: packetcoders/action-setup-cache-python-poetry@main
+      # Setup python
+      - name: Set up Python 3.10
+        uses: actions/setup-python@v5
         with:
-          python-version: 3.9
-          poetry-version: 1.8.3
+          python-version: "3.10"
+
+      - name: Install dependencies
+        run: |
+          curl -LsSf https://astral.sh/uv/install.sh | sh
 
-      - name: Install notebook dependencies
-        run: poetry install -E notebook --with docs
+      - name: Install package
+        run: |
+          uv pip install ".[docs,notebook]"
 
       - name: Install pandoc
         run: |
           sudo wget https://github.com/jgm/pandoc/releases/download/3.1.8/pandoc-3.1.8-1-amd64.deb
           sudo dpkg -i pandoc-3.1.8-1-amd64.deb
 
-      # rm because hextof_workflow notebook can not run outside maxwell
       - name: copy tutorial files to docs
         run: |
           cp -r $GITHUB_WORKSPACE/tutorial $GITHUB_WORKSPACE/docs/
-          cp -r $GITHUB_WORKSPACE/sed/config $GITHUB_WORKSPACE/docs/sed
-
+          mkdir -p $GITHUB_WORKSPACE/docs/src/sed
+          cp -r $GITHUB_WORKSPACE/src/sed/config $GITHUB_WORKSPACE/docs/src/sed/
 
       - name: download RAW data
         # if: steps.cache-primes.outputs.cache-hit != 'true'
         run: |
           cd $GITHUB_WORKSPACE/docs
-          poetry run python scripts/download_data.py
+          python scripts/download_data.py
 
       - name: build parquet files
         run: |
           cd $GITHUB_WORKSPACE/docs
-          poetry run python scripts/build_flash_parquets.py
-          poetry run python scripts/build_sxp_parquets.py
-
-      # to be removed later. This theme doesn't support <3.9 python and our lock file contains 3.8
-      - name: install pydata-sphinx-theme
-        run: |
-          poetry run pip install pydata-sphinx-theme
-
-      - name: Change version for develop build
-        if: startsWith(github.ref, 'refs/heads/') && github.ref != 'refs/heads/main'
-        run: |
-          VERSION=`sed -n 's/^version = "\(.*\)".*/\1/p' $GITHUB_WORKSPACE/pyproject.toml`
-          MOD_VERSION=$VERSION".dev0"
-          echo $MOD_VERSION
-          sed -i "s/^version = \"$VERSION\"/version = \"$MOD_VERSION\"/" $GITHUB_WORKSPACE/pyproject.toml
-
-      - name: Change version for release build
-        if: startsWith(github.ref, 'refs/tags/')
-        run: |
-          OLD_VERSION=`sed -n 's/^version = "\(.*\)".*/\1/p' $GITHUB_WORKSPACE/pyproject.toml`
-          NEW_VERSION=`echo ${GITHUB_REF#refs/tags/} | sed -n 's/^v\(.*\)/\1/p'`
-          echo $NEW_VERSION
-          sed -i "s/^version = \"$OLD_VERSION\"/version = \"$NEW_VERSION\"/" $GITHUB_WORKSPACE/pyproject.toml
+          python scripts/build_flash_parquets.py
+          python scripts/build_sxp_parquets.py
 
       - name: build Sphinx docs
-        run: poetry run sphinx-build -b html $GITHUB_WORKSPACE/docs $GITHUB_WORKSPACE/_build
+        run: sphinx-build -b html $GITHUB_WORKSPACE/docs $GITHUB_WORKSPACE/_build
 
       - name: Upload artifact
         uses: actions/upload-artifact@v4
@@ -102,17 +85,12 @@ jobs:
     needs: build
     steps:
       - name: Checkout docs repo
-        uses: actions/checkout@v2
+        uses: actions/checkout@v4
         with:
           repository: ${{ github.repository_owner }}/docs
           token: ${{ secrets.GITHUB_TOKEN }}
           path: 'docs-repo'
 
-      - name: Set up Python 3.9
-        uses: actions/setup-python@v4
-        with:
-          python-version: 3.9
-
       - name: Setup SSH
         uses: webfactory/ssh-agent@v0.9.0
         with:
@@ -129,11 +107,16 @@ jobs:
         run: |
           if [[ $GITHUB_REF == refs/tags/* ]]; then
             VERSION=${GITHUB_REF#refs/tags/}
-            echo "folder=sed/$VERSION" >> $GITHUB_OUTPUT
-            rm docs-repo/sed/stable
-            rm -rf docs-repo/sed/latest
-            ln -s -r docs-repo/sed/$VERSION docs-repo/sed/stable
-            ln -s -r docs-repo/sed/$VERSION docs-repo/sed/latest
+              echo "folder=sed/$VERSION" >> $GITHUB_OUTPUT
+            if [[ $VERSION == *a* ]]; then
+              rm -rf docs-repo/sed/latest
+              ln -s -r docs-repo/sed/$VERSION docs-repo/sed/latest
+            else
+              rm -rf docs-repo/sed/stable
+              rm -rf docs-repo/sed/latest
+              ln -s -r docs-repo/sed/$VERSION docs-repo/sed/stable
+              ln -s -r docs-repo/sed/$VERSION docs-repo/sed/latest
+            fi
           elif [[ $GITHUB_REF == refs/heads/main ]]; then
             rm -rf docs-repo/sed/latest
             echo "folder=sed/latest" >> $GITHUB_OUTPUT
diff --git a/.github/workflows/linting.yml b/.github/workflows/linting.yml
index ed686550..36ef4282 100644
--- a/.github/workflows/linting.yml
+++ b/.github/workflows/linting.yml
@@ -3,8 +3,9 @@ name: linting
 # Triggers the workflow on push for all branches
 on:
   push:
-    paths-ignore:
-      pyproject.toml
+
+env:
+  UV_SYSTEM_PYTHON: true
 
 jobs:
   lint:
@@ -13,27 +14,34 @@ jobs:
       # Check out repo and set up Python
       - uses: actions/checkout@v4
         with:
-          lfs: true
+          fetch-depth: 0
 
-      # Use cached python and dependencies, install poetry
-      - name: "Setup Python, Poetry and Dependencies"
-        uses: packetcoders/action-setup-cache-python-poetry@main
+      # Setup python
+      - name: Set up Python 3.10
+        uses: actions/setup-python@v5
         with:
-          python-version: 3.8
-          poetry-version: 1.2.2
+          python-version: "3.10"
+
+      - name: Install dependencies
+        run: |
+          curl -LsSf https://astral.sh/uv/install.sh | sh
+
+      - name: Install package
+        run: |
+          uv pip install ".[dev]"
 
       # Linting steps, execute all linters even if one fails
       - name: ruff
         run:
-          poetry run ruff sed tests
+          ruff src/sed tests
       - name: ruff formatting
         if: ${{ always() }}
         run:
-          poetry run ruff format --check sed tests
+          ruff format --check src/sed tests
       - name: mypy
         if: ${{ always() }}
         run:
-          poetry run mypy sed tests
+          mypy src/sed tests
       - name: spellcheck
         if: ${{ always() }}
         uses: streetsidesoftware/cspell-action@v6
diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
index b90abfb6..def39ef9 100644
--- a/.github/workflows/release.yml
+++ b/.github/workflows/release.yml
@@ -1,22 +1,10 @@
 name: Publish to PyPI
 
-# Workflow runs prerelease job on every push to main branch
-# and a release job on every tag push.
-# A publish job is executed on every successful prerelease or release job
-# And if publish is successful, the version is also updated in the pyproject.toml file and pushed to main branch
-  # Workflow does not trigger itself as it only changes pyproject.toml, which is not in paths for this workflow
+# Workflow runs a release job on every published tag.
 # The package is distributed as sed-processor
 on:
-  push:
-    branches:
-      - main
-    tags:
-      - v[0-9]+.[0-9]+.[0-9]+
-    paths:
-      - sed/**/*
-      - .github/workflows/release.yml
-  # Allows you to run this workflow manually from the Actions tab
-  workflow_dispatch:
+  release:
+    types: [published]
 
 # Allow only one concurrent deployment, skipping runs queued between the run in-progress and latest queued.
 # However, do NOT cancel in-progress runs as we want to allow these production deployments to complete.
@@ -24,149 +12,43 @@ concurrency:
   group: "release"
   cancel-in-progress: false
 
-jobs:
-  prerelease:
-    if: github.ref == 'refs/heads/main'
-    runs-on: ubuntu-latest
-    outputs:
-      version: ${{ steps.version.outputs.version }}
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          lfs: true
-          path: 'sed-processor'
-
-      - name: "Setup Python, Poetry and Dependencies"
-        uses: zain-sohail/action-setup-cache-python-poetry@main
-        with:
-          python-version: 3.8
-          poetry-version: 1.2.2
-          working-directory: sed-processor
-
-      - name: Change to distribution name in toml file
-        run: |
-          cd sed-processor
-          sed -i 's/name = "sed"/name = "sed-processor"/' pyproject.toml
-
-      - name: bump pre-release version
-        id: version
-        working-directory: sed-processor
-        run: |
-          VERSION=$(poetry version -s prerelease)
-          echo "version=$VERSION" >> $GITHUB_OUTPUT
-          poetry build
-
-      - name: Upload artifact
-        uses: actions/upload-artifact@v4
-        with:
-          name: dist
-          path: sed-processor/dist
-
-      - name: Upload pyproject.toml
-        uses: actions/upload-artifact@v4
-        with:
-          name: pyproject
-          path: sed-processor/pyproject.toml
+env:
+  UV_SYSTEM_PYTHON: true
 
+jobs:
   release:
-    if: startsWith(github.ref, 'refs/tags/')
+    name: Upload release to PyPI
     runs-on: ubuntu-latest
-    outputs:
-      version: ${{ steps.version.outputs.version }}
+    environment:
+      name: pypi
+      url: https://pypi.org/p/sed-processor
+    permissions:
+      id-token: write
+
     steps:
       - uses: actions/checkout@v4
         with:
-          lfs: true
+          fetch-depth: 0
           path: 'sed-processor'
 
-      - name: "Setup Python, Poetry and Dependencies"
-        uses: zain-sohail/action-setup-cache-python-poetry@main
+      - name: Set up Python
+        uses: actions/setup-python@v5
         with:
-          python-version: 3.8
-          poetry-version: 1.2.2
-          working-directory: sed-processor
+          python-version: "3.x"
 
-      - name: Change to distribution name in toml file
+      - name: Install dependencies
         run: |
-          cd sed-processor
-          sed -i 's/name = "sed"/name = "sed-processor"/' pyproject.toml
+          curl -LsSf https://astral.sh/uv/install.sh | sh
+          uv pip install build
 
-      - name: Bump release version and build
-        id: version
+      - name: Build package
         working-directory: sed-processor
         run: |
-          VERSION=$(echo ${GITHUB_REF#refs/tags/v} | sed 's/-.*//')
-          echo "version=$VERSION" >> $GITHUB_OUTPUT
-          poetry version $VERSION
-          poetry build
-
-      - name: Upload artifact
-        uses: actions/upload-artifact@v4
-        with:
-          name: dist
-          path: sed-processor/dist
-
-      - name: Upload pyproject.toml
-        uses: actions/upload-artifact@v4
-        with:
-          name: pyproject
-          path: sed-processor/pyproject.toml
-
-  publish:
-    needs: [prerelease, release]
-    if: always() && (needs.prerelease.result == 'success' || needs.release.result == 'success')
-    runs-on: ubuntu-latest
-    outputs:
-      version: ${{ needs.prerelease.outputs.version || needs.release.outputs.version }}
-    environment:
-        name: pypi
-        url: https://pypi.org/p/sed-processor
-    permissions:
-        id-token: write
-
-    steps:
-      - name: Download a single artifact
-        uses: actions/download-artifact@v4
-        with:
-          name: dist
+          git reset --hard HEAD
+          python -m build
 
       - name: Publish package distributions to PyPI
         uses: pypa/gh-action-pypi-publish@release/v1
         with:
-          packages-dir: .
-
-  bump-version:
-    needs: publish
-    if: always() && (needs.publish.result == 'success')
-    runs-on: ubuntu-latest
-    steps:
-      - name: Generate a token
-        id: generate_token
-        uses: actions/create-github-app-token@v1
-        with:
-          app-id: ${{ secrets.APP_ID }}
-          private-key: ${{ secrets.APP_PRIVATE_KEY }}
-
-      - uses: actions/checkout@v4
-        with:
-          lfs: true
-          token: ${{ steps.generate_token.outputs.token }}
-
-      - name: Download pyproject.toml
-        uses: actions/download-artifact@v4
-        with:
-          name: pyproject
-
-      - name: Commit files
-        run: |
-          cd sed/
-          git config --local user.email "bump[bot]@users.noreply.github.com"
-          git config --local user.name "bump[bot]"
-          git add $GITHUB_WORKSPACE/pyproject.toml
-          git commit -m "bump version to ${{ needs.publish.outputs.version  }}"
-
-      - name: Push changes
-        uses: ad-m/github-push-action@master
-        with:
-          github_token:  ${{ steps.generate_token.outputs.token }}
-          branch: main
+          verbose: true
+          packages-dir: sed-processor/dist
diff --git a/.github/workflows/testing_coverage.yml b/.github/workflows/testing_coverage.yml
index ddf7e2a9..46d04e3a 100644
--- a/.github/workflows/testing_coverage.yml
+++ b/.github/workflows/testing_coverage.yml
@@ -4,36 +4,39 @@ name: pytest and coverage report
 on:
   push:
     branches: [ main ]
-    paths-ignore:
-      pyproject.toml
   pull_request:
-    branches:
-      - main
+    branches: [ main ]
+
+env:
+  UV_SYSTEM_PYTHON: true
 
 jobs:
   pytest:
     runs-on: ubuntu-latest
     steps:
       # Check out repo and set up Python
-      - name: Check out the repository
-        uses: actions/checkout@v4
+      - uses: actions/checkout@v4
         with:
-          lfs: true
+          fetch-depth: 0
 
-      # Use cached python and dependencies, install poetry
-      - name: "Setup Python, Poetry and Dependencies"
-        uses: packetcoders/action-setup-cache-python-poetry@main
+      # Setup python
+      - name: Set up Python 3.10
+        uses: actions/setup-python@v5
         with:
-          python-version: 3.8
-          poetry-version: 1.2.2
+          python-version: "3.10"
 
-      - name: Install project dependencies
-        run: poetry install
+      - name: Install dependencies
+        run: |
+          curl -LsSf https://astral.sh/uv/install.sh | sh
+
+      - name: Install package
+        run: |
+          uv pip install -e ".[dev]"
 
       # Run pytest with coverage report, saving to xml
-      - name: Run tests on python 3.8
+      - name: Run tests on python 3.10
         run: |
-          poetry run pytest --cov --cov-report xml:cobertura.xml --full-trace --show-capture=no -sv -n auto tests/
+          pytest --cov --cov-report xml:cobertura.xml --full-trace --show-capture=no -sv -n auto tests/
 
       # Take report and upload to coveralls
       - name: Coveralls
diff --git a/.github/workflows/testing_multiversion.yml b/.github/workflows/testing_multiversion.yml
index d3610372..7a9daab0 100644
--- a/.github/workflows/testing_multiversion.yml
+++ b/.github/workflows/testing_multiversion.yml
@@ -1,36 +1,43 @@
-name: unit tests [Python 3.8|3.9|3.10|3.11]
+# Tests for all supported versions [Python 3.9|3.10|3.11|3.12]
+name: Unit Tests
 
 on:
+  schedule:
+    - cron:  '0 1 * * 1'
   workflow_dispatch:
   push:
     branches: [ main ]
-    paths-ignore:
-      pyproject.toml
+
+env:
+  UV_SYSTEM_PYTHON: true
 
 jobs:
   pytest:
+    runs-on: ubuntu-latest
     # Using matrix strategy
     strategy:
       matrix:
-        python-version: ["3.8", "3.9", "3.10", "3.11.8"]
-    runs-on: ubuntu-latest
+        python-version: ["3.9", "3.10", "3.11", "3.12"]
+
     steps:
       # Check out repo and set up Python
-      - name: Check out the repository
-        uses: actions/checkout@v4
+      - uses: actions/checkout@v4
         with:
-          lfs: true
+          fetch-depth: 0
 
-      - name: "Setup Python, Poetry and Dependencies"
-        uses: packetcoders/action-setup-cache-python-poetry@main
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v5
         with:
-          python-version: ${{matrix.python-version}}
-          poetry-version: 1.2.2
+          python-version: ${{ matrix.python-version }}
 
-      - name: Install project dependencies
-        run: poetry install
+      - name: Install dependencies
+        run: |
+          curl -LsSf https://astral.sh/uv/install.sh | sh
+
+      - name: Install package
+        run: |
+          uv pip install ".[dev]"
 
-      # Use cached python and dependencies, install poetry
       - name: Run tests on python ${{matrix.python-version}}
         run: |
-          poetry run pytest --full-trace --show-capture=no -sv -n auto tests/
+          pytest --full-trace --show-capture=no -sv -n auto tests/
diff --git a/.github/workflows/update_dependencies.yml b/.github/workflows/update_dependencies.yml
deleted file mode 100644
index 3735d2e6..00000000
--- a/.github/workflows/update_dependencies.yml
+++ /dev/null
@@ -1,64 +0,0 @@
-name: Update dependencies in poetry lockfile
-
-on:
-  schedule:
-    - cron:  '0 1 * * 1'
-  workflow_dispatch:
-  push:
-    branches: main
-    paths:
-      - .github/workflows/update_dependencies.yml
-
-jobs:
-  update_dependencies:
-    runs-on: ubuntu-latest
-    steps:
-      # Check out repo and set up Python
-      - uses: actions/checkout@v4
-        with:
-          lfs: true
-
-      - uses: tibdex/github-app-token@v1
-        id: generate-token
-        with:
-          app_id: ${{ secrets.APP_ID }}
-          private_key: ${{ secrets.APP_PRIVATE_KEY }}
-
-      # Use cached python and dependencies, install poetry
-      - name: "Setup Python, Poetry and Dependencies"
-        uses: packetcoders/action-setup-cache-python-poetry@main
-        with:
-          python-version: 3.8
-          poetry-version: 1.2.2
-
-      # update poetry lockfile
-      - name: "Update poetry lock file"
-        id: update
-        run: |
-          poetry self update
-          exec 5>&1
-          UPDATE_OUTPUT=$(poetry update|tee >(cat - >&5))
-          echo "UPDATE_OUTPUT<<EOF" >> $GITHUB_OUTPUT
-          echo "$UPDATE_OUTPUT" >> $GITHUB_OUTPUT
-          echo "EOF" >> $GITHUB_OUTPUT
-
-      - name: Obtain git status
-        id: status
-        run: |
-          exec 5>&1
-          STATUS=$(git status|tee >(cat - >&5))
-          echo "STATUS<<EOF" >> $GITHUB_OUTPUT
-          echo "$STATUS" >> $GITHUB_OUTPUT
-          echo "EOF" >> $GITHUB_OUTPUT
-
-      # create pull request if necessary
-      - name: "Create Pull Request"
-        uses: peter-evans/create-pull-request@v5
-        if: ${{ contains(steps.status.outputs.STATUS, 'poetry.lock') || contains(steps.status.outputs.STATUS, 'requirements.txt')}}
-        with:
-          token: ${{ steps.generate-token.outputs.token }}
-          commit-message: Update dependencies
-          title: "Update dependencies"
-          body: |
-            Dependency updates using Poetry:
-            ${{ steps.update.outputs.UPDATE_OUTPUT }}
diff --git a/.gitignore b/.gitignore
index e7828d0f..8cd12dcc 100644
--- a/.gitignore
+++ b/.gitignore
@@ -12,6 +12,10 @@
 **/buffer/*
 **/sed_config.yaml
 **/datasets.json
+copy_yaml_to_json.ipynb
+
+# local copies
+**/*.local.*
 
 # Byte-compiled / optimized / DLL files
 __pycache__/
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index e94afa05..23dd1b35 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -27,12 +27,12 @@ repos:
     rev: v3.8.2
     hooks:
     -   id: reorder-python-imports
-        args: [--application-directories, '.:src', --py36-plus]
+        args: [--application-directories, '.:src', --py39-plus]
 -   repo: https://github.com/asottile/pyupgrade
-    rev: v2.37.3
+    rev: v3.16.0
     hooks:
     -   id: pyupgrade
-        args: [--py36-plus]
+        args: [--py39-plus]
 -   repo: https://github.com/asottile/add-trailing-comma
     rev: v2.2.3
     hooks:
diff --git a/MANIFEST.in b/MANIFEST.in
new file mode 100644
index 00000000..d9ea7561
--- /dev/null
+++ b/MANIFEST.in
@@ -0,0 +1,5 @@
+prune *
+exclude *
+recursive-include src/sed *.py
+include pyproject.toml README.md
+graft src/sed/config/
diff --git a/README.md b/README.md
index ae4b766f..c4213269 100644
--- a/README.md
+++ b/README.md
@@ -18,7 +18,7 @@ Finally, in contains several export routines, including export into the [NeXus](
 # Installation
 
 ## Prerequisites
-- Python 3.8+
+- Python 3.9+
 - pip
 
 ## Steps
diff --git a/benchmarks/benchmark_sed.py b/benchmarks/benchmark_sed.py
index 3b633c30..a8d82e69 100644
--- a/benchmarks/benchmark_sed.py
+++ b/benchmarks/benchmark_sed.py
@@ -18,6 +18,7 @@
 from tests.loader.test_loaders import get_loader_name_from_loader_object
 
 package_dir = os.path.dirname(find_spec("sed").origin)
+benchmark_dir = os.path.dirname(__file__)
 
 
 num_cores = min(20, psutil.cpu_count())
@@ -32,10 +33,10 @@
 )
 dataframe = dask.dataframe.from_dask_array(array, columns=axes)
 
-test_data_dir = os.path.join(package_dir, "..", "tests", "data")
+test_data_dir = os.path.join(benchmark_dir, "..", "tests", "data")
 runs = {"generic": None, "mpes": ["30", "50"], "flash": ["43878"], "sxp": ["0016"]}
 
-targets = load_config(package_dir + "/../benchmarks/benchmark_targets.yaml")
+targets = load_config(benchmark_dir + "/benchmark_targets.yaml")
 
 
 def test_binning_1d() -> None:
@@ -54,12 +55,12 @@ def test_binning_1d() -> None:
     )
     result = timer.repeat(5, number=1)
     print(result)
-    assert min(result) < targets["binning_1d"] * 1.25 # allows 25% error margin
+    assert min(result) < targets["binning_1d"] * 1.25  # allows 25% error margin
     # update targets if > 20% improvement occurs beyond old bestmark
     if np.mean(result) < 0.8 * targets["binning_1d"]:
         print(f"Updating targets for 'binning_1d' to {float(np.mean(result))}")
         targets["binning_1d"] = float(np.mean(result))
-        save_config(targets, package_dir + "/../benchmarks/benchmark_targets.yaml")
+        save_config(targets, benchmark_dir + "/benchmark_targets.yaml")
 
 
 def test_binning_4d() -> None:
@@ -78,12 +79,12 @@ def test_binning_4d() -> None:
     )
     result = timer.repeat(5, number=1)
     print(result)
-    assert min(result) < targets["binning_4d"] * 1.25 # allows 25% error margin
+    assert min(result) < targets["binning_4d"] * 1.25  # allows 25% error margin
     # update targets if > 20% improvement occurs beyond old bestmark
     if np.mean(result) < 0.8 * targets["binning_4d"]:
         print(f"Updating targets for 'binning_4d' to {float(np.mean(result))}")
         targets["binning_4d"] = float(np.mean(result))
-        save_config(targets, package_dir + "/../benchmarks/benchmark_targets.yaml")
+        save_config(targets, benchmark_dir + "/benchmark_targets.yaml")
 
 
 def test_splinewarp() -> None:
@@ -95,6 +96,7 @@ def test_splinewarp() -> None:
         user_config={},
         system_config={},
         verbose=True,
+        verify_config=False,
     )
     processor.apply_momentum_correction()
     timer = timeit.Timer(
@@ -103,12 +105,12 @@ def test_splinewarp() -> None:
     )
     result = timer.repeat(5, number=1)
     print(result)
-    assert min(result) < targets["inv_dfield"] * 1.25 # allows 25% error margin
+    assert min(result) < targets["inv_dfield"] * 1.25  # allows 25% error margin
     # update targets if > 20% improvement occurs beyond old bestmark
     if np.mean(result) < 0.8 * targets["inv_dfield"]:
         print(f"Updating targets for 'inv_dfield' to {float(np.mean(result))}")
         targets["inv_dfield"] = float(np.mean(result))
-        save_config(targets, package_dir + "/../benchmarks/benchmark_targets.yaml")
+        save_config(targets, benchmark_dir + "/benchmark_targets.yaml")
 
 
 def test_workflow_1d() -> None:
@@ -120,7 +122,9 @@ def test_workflow_1d() -> None:
         user_config={},
         system_config={},
         verbose=True,
+        verify_config=False,
     )
+    processor.dataframe["sampleBias"] = 16.7
     processor.add_jitter()
     processor.apply_momentum_correction()
     processor.apply_momentum_calibration()
@@ -137,12 +141,12 @@ def test_workflow_1d() -> None:
     )
     result = timer.repeat(5, number=1)
     print(result)
-    assert min(result) < targets["workflow_1d"] * 1.25 # allows 25% error margin
+    assert min(result) < targets["workflow_1d"] * 1.25  # allows 25% error margin
     # update targets if > 20% improvement occurs beyond old bestmark
     if np.mean(result) < 0.8 * targets["workflow_1d"]:
         print(f"Updating targets for 'workflow_1d' to {float(np.mean(result))}")
         targets["workflow_1d"] = float(np.mean(result))
-        save_config(targets, package_dir + "/../benchmarks/benchmark_targets.yaml")
+        save_config(targets, benchmark_dir + "/benchmark_targets.yaml")
 
 
 def test_workflow_4d() -> None:
@@ -154,7 +158,9 @@ def test_workflow_4d() -> None:
         user_config={},
         system_config={},
         verbose=True,
+        verify_config=False,
     )
+    processor.dataframe["sampleBias"] = 16.7
     processor.add_jitter()
     processor.apply_momentum_correction()
     processor.apply_momentum_calibration()
@@ -171,12 +177,12 @@ def test_workflow_4d() -> None:
     )
     result = timer.repeat(5, number=1)
     print(result)
-    assert min(result) < targets["workflow_4d"] * 1.25 # allows 25% error margin
+    assert min(result) < targets["workflow_4d"] * 1.25  # allows 25% error margin
     # update targets if > 20% improvement occurs beyond old bestmark
     if np.mean(result) < 0.8 * targets["workflow_4d"]:
         print(f"Updating targets for 'workflow_4d' to {float(np.mean(result))}")
         targets["workflow_4d"] = float(np.mean(result))
-        save_config(targets, package_dir + "/../benchmarks/benchmark_targets.yaml")
+        save_config(targets, benchmark_dir + "/benchmark_targets.yaml")
 
 
 @pytest.mark.parametrize("loader", get_all_loaders())
@@ -197,7 +203,7 @@ def test_loader_compute(loader: BaseLoader) -> None:
         )
         result = timer.repeat(20, number=1)
         print(result)
-        assert min(result) < targets[f"loader_compute_{loader_name}"] * 1.25 # allows 25% margin
+        assert min(result) < targets[f"loader_compute_{loader_name}"] * 1.25  # allows 25% margin
         # update targets if > 20% improvement occurs beyond old bestmark
         if np.mean(result) < 0.8 * targets[f"loader_compute_{loader_name}"]:
             print(
@@ -205,4 +211,4 @@ def test_loader_compute(loader: BaseLoader) -> None:
                 f"to {float(np.mean(result))}",
             )
             targets[f"loader_compute_{loader_name}"] = float(np.mean(result))
-            save_config(targets, package_dir + "/../benchmarks/benchmark_targets.yaml")
+            save_config(targets, benchmark_dir + "/benchmark_targets.yaml")
diff --git a/benchmarks/benchmark_targets.yaml b/benchmarks/benchmark_targets.yaml
index b2b451cb..1b8e4939 100644
--- a/benchmarks/benchmark_targets.yaml
+++ b/benchmarks/benchmark_targets.yaml
@@ -1,7 +1,7 @@
 binning_1d: 3.017609174399999
 binning_4d: 9.210316116800005
 inv_dfield: 5.196141159999996
-loader_compute_flash: 0.03584787449999567
+loader_compute_flash: 0.00917599634999533
 loader_compute_mpes: 0.015864623800007395
 loader_compute_sxp: 0.006027440450000654
 workflow_1d: 17.0553120846
diff --git a/cspell.json b/cspell.json
index 5826d5ab..585f1883 100644
--- a/cspell.json
+++ b/cspell.json
@@ -4,7 +4,8 @@
         "./tests/data/*",
         "*.toml",
         "Makefile",
-        "*.bat"
+        "*.bat",
+        "*.egg-info",
     ],
     "dictionaryDefinitions": [
         {
diff --git a/docs/conf.py b/docs/conf.py
index 68ec181c..6c7b066f 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -13,6 +13,8 @@
 
 import tomlkit
 
+from sed import __version__
+
 sys.path.insert(0, os.path.abspath(".."))
 
 # -- Project information -----------------------------------------------------
@@ -22,7 +24,7 @@ def _get_project_meta():
     with open("../pyproject.toml") as pyproject:
         file_contents = pyproject.read()
 
-    return tomlkit.parse(file_contents)["tool"]["poetry"]
+    return tomlkit.parse(file_contents)["project"]
 
 
 # -- Project information -----------------------------------------------------
@@ -32,7 +34,7 @@ def _get_project_meta():
 author = "OpenCOMPES team"
 
 # The short X.Y version
-version = str(pkg_meta["version"])
+version = __version__
 # The full version, including alpha/beta/rc tags
 release = version
 
@@ -93,7 +95,7 @@ def _get_project_meta():
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
 # This pattern also affects html_static_path and html_extra_path.
-exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
+exclude_patterns = ["_build", "Thumbs.db", ".DS_Store", "**.ipynb_checkpoints"]
 
 
 # -- Options for HTML output -------------------------------------------------
diff --git a/docs/misc/contributing.rst b/docs/misc/contributing.rst
index 7aab314a..d638deef 100644
--- a/docs/misc/contributing.rst
+++ b/docs/misc/contributing.rst
@@ -28,13 +28,12 @@ Getting Started
 
 
 
-2. **Install Python and Poetry:**
-   - Ensure you have Python 3.8, 3.9, 3.10 or 3.11 and poetry installed.
+2. **Install uv and Python:** Ensure you have uv and Python 3.9, 3.10, 3.11 or 3.12 installed. You can install it e.g. using the following commands:
 
     .. code-block:: bash
 
-        pip install pipx
-        pipx install poetry
+        curl -LsSf https://astral.sh/uv/install.sh | sh
+        uv python install 3.10
 
 3. **Clone Repository:**
 
@@ -42,25 +41,32 @@ Getting Started
 
         git clone https://github.com/OpenCOMPES/sed.git
 
-4. **Install Dependencies:**
-   - Navigate to the project directory and install the project dependencies (including development ones) using Poetry:
+4. **Set up virtual environment:** Create a python virtual environment, and activate it. You can optionally select the python version, and set the path
 
     .. code-block:: bash
 
-        poetry install --dev
+        uv venv -p=3.10 .venv
+        source .venv/bin/activate
+
+
+5. **Install Dependencies:** Navigate to the project directory and install the project and its dependencies (including development ones) in "editable" mode using uv. Optionally, the jupyter notebook can be installed as well:
+
+    .. code-block:: bash
+
+        uv pip install -e .[dev,notebook]
 
 
 Development Workflow
 =====================
 
 .. note::
-   This guide assumes that you have Python (version 3.8, 3.9, 3.10, 3.11) and poetry with dev dependencies installed on your machine.
+   This guide assumes that you followed the previous steps and have uv, python and the package with dev dependencies installed on your machine.
 
 1. **Install pre-commit hooks:** To ensure your code is formatted correctly, install pre-commit hooks:
 
     .. code-block:: bash
 
-        pip install pre-commit
+        pre-commit install
 
 
 2. **Create a Branch:** Create a new branch for your feature or bug fix and make changes:
@@ -86,7 +92,7 @@ Development Workflow
         git commit -a -m "Your commit message"
 
 
-6. **Push Changes:** Push your changes to your fork:
+6. **Push Changes:** Push your changes to your new branch:
 
     .. code-block:: bash
 
diff --git a/docs/misc/maintain.rst b/docs/misc/maintain.rst
index cf8d0a93..b7e2068a 100644
--- a/docs/misc/maintain.rst
+++ b/docs/misc/maintain.rst
@@ -11,8 +11,8 @@ Users can generate documentation locally using the following steps:
 
 .. code-block:: bash
 
-    pip install pipx
-    pipx install poetry
+    curl -LsSf https://astral.sh/uv/install.sh | sh
+    uv python install 3.10
 
 1. **Clone Repository:**
 
@@ -39,19 +39,20 @@ Doing this step will slow down the build process significantly. It also requires
 
 .. code-block:: bash
 
-    poetry shell
+    uv venv -p=3.10 .venv
+    source .venv/bin/activate
 
 6. **Install Dependencies:**
 
 .. code-block:: bash
 
-    poetry install --with docs
+    uv pip install -e .[docs]
 
 7. **Build Documentation:**
 
 .. code-block:: bash
 
-    poetry run sphinx-build -b html docs _build
+    sphinx-build -b html docs _build
 
 8. **View Documentation:**
 
@@ -104,14 +105,14 @@ Here's how the workflow works:
    - The workflow is divided into two jobs: build and deploy.
 
      a. **Build Job:**
-        - Sets up the build environment, checks out the repository, and installs necessary dependencies using Poetry.
+        - Sets up the build environment, checks out the repository, and installs necessary dependencies using uv.
         - Installs notebook dependencies and Pandoc.
-        - Copies tutorial files to the docs directory and removes unnecessary notebooks.
+        - Copies tutorial files to the docs directory.
         - Downloads RAW data for tutorials.
         - Builds Sphinx documentation.
 
      b. **Deploy Job:**
-        - Deploys the built documentation to GitHub Pages.
+        - Deploys the built documentation to GitHub Pages repository.
 
 5. **Manual Execution:**
    - To manually trigger the workflow, go to the Actions tab on GitHub.
@@ -147,10 +148,5 @@ To create a release, follow these steps:
 
 - *Release Job:*
     - This workflow is responsible for versioning and releasing the package.
-    - A release job runs on every git tag push (e.g., `git tag v0.1.5`) and publishes the package to PyPI.
-    - If the publish is successful, the version in the `pyproject.toml` file is updated and pushed to the main branch.
-
-- *Prerelease Job:*
-    - This workflow is triggered automatically on every pull request (PR) to the main branch.
-    - It increments the version number for prerelease (e.g., from 0.1.5 to 0.1.6a0 to 0.1.6a1) and publishes the package to PyPI.
-    - If the publish is successful, the version in the `pyproject.toml` file is updated and pushed to the main branch.
+    - A release job runs on every git release and publishes the package to PyPI.
+    - The package version is dynamically obtained from the most recent git tag.
diff --git a/docs/scripts/build_flash_parquets.py b/docs/scripts/build_flash_parquets.py
index 8a679969..eef402b3 100644
--- a/docs/scripts/build_flash_parquets.py
+++ b/docs/scripts/build_flash_parquets.py
@@ -1,10 +1,12 @@
-from pathlib import Path
+import os
+from importlib.util import find_spec
 
-import sed
 from sed import SedProcessor
 from sed.dataset import dataset
 
-config_file = Path(sed.__file__).parent / "config/flash_example_config.yaml"
+package_dir = os.path.dirname(find_spec("sed").origin)
+
+config_file = package_dir + "/config/flash_example_config.yaml"
 
 dataset.get("Gd_W110", root_dir="./tutorial")
 data_path = dataset.dir
@@ -13,8 +15,8 @@
 config_override = {
     "core": {
         "paths": {
-            "data_raw_dir": data_path,
-            "data_parquet_dir": data_path + "/processed/",
+            "raw": data_path,
+            "processed": data_path + "/processed/",
         },
     },
 }
@@ -35,8 +37,8 @@
 config_override = {
     "core": {
         "paths": {
-            "data_raw_dir": data_path,
-            "data_parquet_dir": data_path + "/processed/",
+            "raw": data_path,
+            "processed": data_path + "/processed/",
         },
     },
 }
diff --git a/docs/scripts/build_sxp_parquets.py b/docs/scripts/build_sxp_parquets.py
index dd870148..5dcbcc74 100644
--- a/docs/scripts/build_sxp_parquets.py
+++ b/docs/scripts/build_sxp_parquets.py
@@ -1,10 +1,12 @@
-from pathlib import Path
+import os
+from importlib.util import find_spec
 
-import sed
 from sed import SedProcessor
 from sed.dataset import dataset
 
-config_file = Path(sed.__file__).parent / "config/sxp_example_config.yaml"
+package_dir = os.path.dirname(find_spec("sed").origin)
+
+config_file = package_dir + "/config/sxp_example_config.yaml"
 
 dataset.get("Au_Mica", root_dir="./tutorial")
 data_path = dataset.dir
@@ -13,8 +15,8 @@
 config_override = {
     "core": {
         "paths": {
-            "data_raw_dir": data_path,
-            "data_parquet_dir": data_path + "/processed/",
+            "raw": data_path,
+            "processed": data_path + "/processed/",
         },
     },
 }
diff --git a/docs/sed/dataset.rst b/docs/sed/dataset.rst
index b0147464..e282423e 100644
--- a/docs/sed/dataset.rst
+++ b/docs/sed/dataset.rst
@@ -318,7 +318,7 @@ we can remove one instance
 Default datasets.json
 ---------------------
 
-.. literalinclude::  ../../sed/dataset/datasets.json
+.. literalinclude::  ../../src/sed/config/datasets.json
    :language: json
 
 API
diff --git a/docs/user_guide/advanced_topics.md b/docs/user_guide/advanced_topics.md
deleted file mode 100644
index d35c19ce..00000000
--- a/docs/user_guide/advanced_topics.md
+++ /dev/null
@@ -1,8 +0,0 @@
-```{toctree}
-:maxdepth: 1
-../tutorial/6_binning_with_time-stamped_data
-../tutorial/7_correcting_orthorhombic_symmetry
-../tutorial/8_jittering_tutorial
-../tutorial/10_hextof_workflow_trXPS_bam_correction
-../tutorial/11_hextof_workflow_trXPS_energy_calibration_using_SB
-```
diff --git a/docs/user_guide/config.md b/docs/user_guide/config.md
index 7fb62f7d..27c53d67 100644
--- a/docs/user_guide/config.md
+++ b/docs/user_guide/config.md
@@ -4,26 +4,26 @@ The config module contains a mechanism to collect configuration parameters from
 It will load an (optional) provided config file, or alternatively use a passed python dictionary as initial config dictionary, and subsequently look for the following additional config files to load:
 
 * ``folder_config``: A config file of name :file:`sed_config.yaml` in the current working directory. This is mostly intended to pass calibration parameters of the workflow between different notebook instances.
-* ``user_config``: A config file provided by the user, stored as :file:`.sed/config.yaml` in the current user's home directly. This is intended to give a user the option for individual configuration modifications of system settings.
-* ``system_config``: A config file provided by the system administrator, stored as :file:`/etc/sed/config.yaml` on Linux-based systems, and :file:`%ALLUSERSPROFILE%/sed/config.yaml` on Windows. This should provide all necessary default parameters for using the sed processor with a given setup. For an example for an mpes setup, see :ref:`example_config`
+* ``user_config``: A config file provided by the user, stored as :file:`.config/sed/config_v1.yaml` in the current user's home directly. This is intended to give a user the option for individual configuration modifications of system settings.
+* ``system_config``: A config file provided by the system administrator, stored as :file:`/etc/sed/config_v1.yaml` on Linux-based systems, and :file:`%ALLUSERSPROFILE%/sed/config_v1.yaml` on Windows. This should provide all necessary default parameters for using the sed processor with a given setup. For an example for an mpes setup, see :ref:`example_config`
 * ``default_config``: The default configuration shipped with the package. Typically, all parameters here should be overwritten by any of the other configuration files.
 
 The config mechanism returns the combined dictionary, and reports the loaded configuration files. In order to disable or overwrite any of the configuration files, they can be also given as optional parameters (path to a file, or python dictionary).
 
 ## Default configuration settings
 
-```{literalinclude} ../../sed/config/default.yaml
+```{literalinclude} ../../src/sed/config/default.yaml
 :language: yaml
 ```
 
 ## Example configuration file for mpes (METIS momentum microscope at FHI-Berlin)
 
-```{literalinclude} ../../sed/config/mpes_example_config.yaml
+```{literalinclude} ../../src/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
+```{literalinclude} ../../src/sed/config/flash_example_config.yaml
 :language: yaml
 ```
diff --git a/docs/user_guide/index.md b/docs/user_guide/index.md
index 16d36aaf..40059b94 100644
--- a/docs/user_guide/index.md
+++ b/docs/user_guide/index.md
@@ -22,8 +22,11 @@ config
 ```
 
 ## Advanced Topics
-
 ```{toctree}
 :maxdepth: 1
-advanced_topics
+../tutorial/6_binning_with_time-stamped_data
+../tutorial/7_correcting_orthorhombic_symmetry
+../tutorial/8_jittering_tutorial
+../tutorial/10_hextof_workflow_trXPS_bam_correction
+../tutorial/11_hextof_workflow_trXPS_energy_calibration_using_SB
 ```
diff --git a/docs/user_guide/installation.md b/docs/user_guide/installation.md
index 8c3f44dd..44d68601 100644
--- a/docs/user_guide/installation.md
+++ b/docs/user_guide/installation.md
@@ -23,10 +23,10 @@ source .sed-venv/bin/activate
 - Install `sed`, distributed as `sed-processor` on PyPI:
 
 ```bash
-pip install sed-processor[all]
+pip install sed-processor[notebook]
 ```
 
-- If you do not use Jupyter Notebook or Jupyter Lab, you can skip the installing those dependencies:
+- If you do not use Jupyter Notebook or Jupyter Lab, you can skip the installation of those dependencies:
 
 ```bash
 pip install sed-processor
diff --git a/poetry.lock b/poetry.lock
deleted file mode 100644
index 84ce22e9..00000000
--- a/poetry.lock
+++ /dev/null
@@ -1,5221 +0,0 @@
-# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand.
-
-[[package]]
-name = "aiofiles"
-version = "22.1.0"
-description = "File support for asyncio."
-optional = true
-python-versions = ">=3.7,<4.0"
-files = [
-    {file = "aiofiles-22.1.0-py3-none-any.whl", hash = "sha256:1142fa8e80dbae46bb6339573ad4c8c0841358f79c6eb50a493dceca14621bad"},
-    {file = "aiofiles-22.1.0.tar.gz", hash = "sha256:9107f1ca0b2a5553987a94a3c9959fe5b491fdf731389aa5b7b1bd0733e32de6"},
-]
-
-[[package]]
-name = "aiosqlite"
-version = "0.20.0"
-description = "asyncio bridge to the standard sqlite3 module"
-optional = true
-python-versions = ">=3.8"
-files = [
-    {file = "aiosqlite-0.20.0-py3-none-any.whl", hash = "sha256:36a1deaca0cac40ebe32aac9977a6e2bbc7f5189f23f4a54d5908986729e5bd6"},
-    {file = "aiosqlite-0.20.0.tar.gz", hash = "sha256:6d35c8c256637f4672f843c31021464090805bf925385ac39473fb16eaaca3d7"},
-]
-
-[package.dependencies]
-typing_extensions = ">=4.0"
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-    {file = "y_py-0.6.2.tar.gz", hash = "sha256:4757a82a50406a0b3a333aa0122019a331bd6f16e49fed67dca423f928b3fd4d"},
-]
-
-[[package]]
-name = "ypy-websocket"
-version = "0.8.4"
-description = "WebSocket connector for Ypy"
-optional = true
-python-versions = ">=3.7"
-files = [
-    {file = "ypy_websocket-0.8.4-py3-none-any.whl", hash = "sha256:b1ba0dfcc9762f0ca168d2378062d3ca1299d39076b0f145d961359121042be5"},
-    {file = "ypy_websocket-0.8.4.tar.gz", hash = "sha256:43a001473f5c8abcf182f603049cf305cbc855ad8deaa9dfa0f3b5a7cea9d0ff"},
-]
-
-[package.dependencies]
-aiofiles = ">=22.1.0,<23"
-aiosqlite = ">=0.17.0,<1"
-y-py = ">=0.6.0,<0.7.0"
-
-[package.extras]
-test = ["mypy", "pre-commit", "pytest", "pytest-asyncio", "websockets (>=10.0)"]
-
-[[package]]
-name = "zipp"
-version = "3.20.2"
-description = "Backport of pathlib-compatible object wrapper for zip files"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "zipp-3.20.2-py3-none-any.whl", hash = "sha256:a817ac80d6cf4b23bf7f2828b7cabf326f15a001bea8b1f9b49631780ba28350"},
-    {file = "zipp-3.20.2.tar.gz", hash = "sha256:bc9eb26f4506fda01b81bcde0ca78103b6e62f991b381fec825435c836edbc29"},
-]
-
-[package.extras]
-check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
-cover = ["pytest-cov"]
-doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-enabler = ["pytest-enabler (>=2.2)"]
-test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
-type = ["pytest-mypy"]
-
-[extras]
-all = ["notebook"]
-notebook = ["ipykernel", "jupyter", "jupyterlab", "jupyterlab-h5web", "notebook"]
-
-[metadata]
-lock-version = "2.0"
-python-versions = ">=3.8, <3.11.9"
-content-hash = "27d6e6f23cf9ee48de7c3af0f71b4865fd4bfc4b1030b625b8ae048f5889abe4"
diff --git a/pyproject.toml b/pyproject.toml
index 653d3f9f..7aec864b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,81 +1,94 @@
-[tool.poetry]
-name = "sed-processor"
-packages = [
-    {include = "sed"}
+[build-system]
+requires = ["setuptools>=64.0.1", "setuptools-scm[toml]>=6.2"]
+build-backend = "setuptools.build_meta"
+
+[tool.setuptools.packages.find]
+where = [
+    "src",
 ]
-version = "0.4.1"
+
+[tool.setuptools_scm]
+
+[project]
+name = "sed-processor"
+dynamic = ["version"]
 description = "Single Event Data Frame Processor: Backend to handle photoelectron resolved datastreams"
-authors = ["OpenCOMPES team <sed-processor@mpes.science>"]
+authors = [
+    {name = "OpenCOMPES team", email = "sed-processor@mpes.science"},
+]
 readme = "README.md"
-repository = "https://github.com/OpenCOMPES/sed"
-documentation = "https://opencompes.github.io/sed/"
 keywords = ["sed", "mpes", "flash", "arpes"]
-license = "MIT"
-
-[tool.poetry.dependencies]
-python = ">=3.8, <3.11.9"
-bokeh = ">=2.4.2"
-dask = ">=2021.12.0, <2023.12.1"
-docutils = "<0.21"
-fastdtw = ">=0.3.4"
-h5py = ">=3.6.0"
-ipympl = ">=0.9.1"
-ipywidgets = "^7.7.1"
-lmfit = ">=1.0.3"
-matplotlib = ">=3.5.1, <3.9.0"
-natsort = ">=8.1.0"
-numba = ">=0.55.1"
-numpy = ">=1.18, <2.0"
-pandas = ">=1.4.1"
-psutil = ">=5.9.0"
-pynxtools-mpes = ">=0.2.0"
-pynxtools = ">=0.8.0"
-pyyaml = ">=6.0.0"
-scipy = ">=1.8.0"
-symmetrize = ">=0.5.5"
-threadpoolctl = ">=3.1.0"
-tifffile = ">=2022.2.9"
-tqdm = ">=4.62.3"
-xarray = ">=0.20.2"
-joblib = ">=1.2.0"
-pyarrow = ">=14.0.1, <17.0"
-jupyter = {version = ">=1.0.0", optional = true}
-ipykernel = {version = ">=6.9.1", optional = true}
-jupyterlab = {version = "^3.4.0", optional = true}
-notebook = {version = ">=6.5.7, <7.0.0", optional = true}
-jupyterlab-h5web = {version = "^8.0.0", extras = ["full"]}
-
-
-[tool.poetry.extras]
-notebook = ["jupyter", "ipykernel", "jupyterlab", "notebook", "jupyterlab-h5web"]
-all = ["notebook"]
-
-[tool.poetry.group.dev.dependencies]
-pytest = ">=7.0.1"
-pytest-cov = ">=3.0.0"
-pytest-xdist = ">=2.5.0"
-pytest-clarity = ">=1.0.1"
-ruff = ">=0.1.7, <0.3.0"
-mypy = ">=1.6.0, <1.10.0"
-types-pyyaml = ">=6.0.12.12"
-types-requests = ">=2.31.0.9"
-pyfakefs = ">=5.3.0"
-requests-mock = "^1.11.0"
-pre-commit = ">=3.0.0"
-
-[tool.poetry.group.docs]
-optional = true
+license = { file = "LICENSE" }
+requires-python = ">=3.9,<3.13"
+classifiers = [
+    "Programming Language :: Python :: 3.9",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+    "License :: OSI Approved :: MIT License",
+    "Operating System :: OS Independent",
+]
+dependencies = [
+    "bokeh>=2.4.2",
+    "dask>=2021.12.0,<2024.8",
+    "fastdtw>=0.3.4",
+    "h5py>=3.6.0",
+    "ipympl>=0.9.1",
+    "ipywidgets>=8.1.5",
+    "lmfit>=1.0.3",
+    "matplotlib>=3.5.1",
+    "natsort>=8.1.0",
+    "numba>=0.55.1",
+    "numpy>=1.18,<2.0",
+    "pandas>=1.4.1",
+    "photutils<2.0",
+    "psutil>=5.9.0",
+    "pynxtools-mpes>=0.2.0",
+    "pynxtools>=0.9.0",
+    "pyyaml>=6.0.0",
+    "scipy>=1.8.0",
+    "symmetrize>=0.5.5",
+    "threadpoolctl>=3.1.0",
+    "tifffile>=2022.2.9",
+    "tqdm>=4.62.3",
+    "xarray>=0.20.2",
+    "joblib>=1.2.0",
+    "pyarrow>=14.0.1,<17.0",
+    "pydantic>=2.8.2",
+]
 
-[tool.poetry.group.docs.dependencies]
-sphinx = ">=7.1.2"
-tomlkit = ">=0.12.0"
-sphinx-autodoc-typehints = ">=1.17.0"
-nbsphinx = ">=0.9.3"
-myst-parser = ">=2.0.0"
+[project.urls]
+repository = "https://github.com/OpenCOMPES/sed"
+documentation = "https://opencompes.github.io/sed/"
 
-[build-system]
-requires = ["poetry-core>=1.0.0"]
-build-backend = "poetry.core.masonry.api"
+[project.optional-dependencies]
+dev = [
+    "pytest>=7.0.1",
+    "pytest-cov>=3.0.0",
+    "pytest-xdist>=2.5.0",
+    "pytest-clarity>=1.0.1",
+    "ruff<0.3.0,>=0.1.7",
+    "mypy<1.10.0,>=1.6.0",
+    "types-pyyaml>=6.0.12.12",
+    "types-requests>=2.31.0.9",
+    "pyfakefs>=5.3.0",
+    "requests-mock>=1.11.0",
+    "pre-commit>=3.0.0",
+]
+docs = [
+    "sphinx>=7.1.2",
+    "tomlkit>=0.12.0",
+    "sphinx-autodoc-typehints>=1.17.0",
+    "nbsphinx>=0.9.3",
+    "myst-parser>=2.0.0",
+    "pydata-sphinx-theme>=0.15.0",
+]
+notebook = [
+    "jupyter>=1.0.0",
+    "ipykernel>=6.9.1",
+    "jupyterlab>=4.0",
+    "jupyterlab-h5web>=8.0.0",
+]
 
 [tool.coverage.report]
 omit = [
@@ -84,7 +97,7 @@ omit = [
 ]
 
 [tool.ruff]
-include = ["sed/*.py", "tests/*.py"]
+include = ["src/*.py", "tests/*.py"]
 lint.select = [
     "E", # pycodestyle
     "W", # pycodestyle
diff --git a/sed/config/flash_example_config.yaml b/sed/config/flash_example_config.yaml
deleted file mode 100644
index 0d2623b3..00000000
--- a/sed/config/flash_example_config.yaml
+++ /dev/null
@@ -1,194 +0,0 @@
-# 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 ID number of the beamtime
-  beamtime_id: 11019101
-  # the year of the beamtime
-  year: 2023
-
-  # The paths to the raw and parquet data directories. If these are not
-  # provided, the loader will try to find the data based on year beamtimeID etc
-  paths:
-    # location of the raw data.
-    data_raw_dir: ""
-    # location of the intermediate parquet files.
-    data_parquet_dir: ""
-
-binning:
-  # Since this will run on maxwell most probably, we have a lot of cores at our disposal
-  num_cores: 100
-
-dataframe:
-  # The name of the DAQ system to use. Necessary to resolve the filenames/paths.
-  daq: fl1user3
-  # The offset correction to the pulseId
-  ubid_offset: 5
-
-  # the number of iterations to fill the pulseId forward.
-  forward_fill_iterations: 2
-  # if true, removes the 3 bits reserved for dldSectorID from the dldTimeSteps column
-  split_sector_id_from_dld_time: True
-  # bits reserved for dldSectorID in the dldTimeSteps column
-  sector_id_reserved_bits: 3
-  # dataframe column containing x coordinates
-  x_column: dldPosX
-  # dataframe column containing corrected x coordinates
-  corrected_x_column: "X"
-  # dataframe column containing kx coordinates
-  kx_column: "kx"
-  # dataframe column containing y coordinates
-  y_column: dldPosY
-  # dataframe column containing corrected y coordinates
-  corrected_y_column: "Y"
-  # dataframe column containing kx coordinates
-  ky_column: "ky"
-  # dataframe column containing time-of-flight data
-  tof_column: dldTimeSteps
-  # dataframe column containing time-of-flight data in ns
-  tof_ns_column: dldTime
-  # dataframe column containing corrected time-of-flight data
-  corrected_tof_column: "tm"
-  # time length of a base time-of-flight bin in seconds
-  tof_binwidth: 2.0576131995767355E-11
-  # binning parameter for time-of-flight data. 2**tof_binning bins per base bin
-  tof_binning: 3  # power of 2, 3 means 8 bins per step
-  # dataframe column containing sector ID. obtained from dldTimeSteps column
-  sector_id_column: dldSectorID
-
-  sector_delays: [0., 0., 0., 0., 0., 0., 0., 0.]
-  # the delay stage column
-  delay_column: delayStage
-  # the corrected pump-probe time axis
-  corrected_delay_column: pumpProbeTime
-  jitter_cols: ["dldPosX", "dldPosY", "dldTimeSteps"]
-
-  units:
-    # These are the units of the columns
-    dldPosX: 'step'
-    dldPosY: 'step'
-    dldTimeSteps: 'step'
-    tof_voltage: 'V'
-    extractorVoltage: 'V'
-    extractorCurrent: 'A'
-    cryoTemperature: 'K'
-    sampleTemperature: 'K'
-    dldTime: 'ns'
-    delay: 'ps'
-    delayStage: 'ps'
-    timeStamp: 's'
-    energy: 'eV'
-    E: 'eV'
-    kx: '1/A'
-    ky: '1/A'
-
-  # 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 multidimensional data, where to slice
-
-  channels:
-    # The timestamp
-    timeStamp:
-      format: per_train
-      group_name: "/uncategorised/FLASH.DIAG/TIMINGINFO/TIME1.BUNCH_FIRST_INDEX.1/"
-
-    # pulse ID is a necessary channel for using the loader.
-    pulseId:
-      format: per_electron
-      group_name: "/uncategorised/FLASH.EXP/HEXTOF.DAQ/DLD1/"
-      slice: 2
-
-    # detector x position
-    dldPosX:
-      format: per_electron
-      group_name: "/uncategorised/FLASH.EXP/HEXTOF.DAQ/DLD1/"
-      slice: 1
-
-    # detector y position
-    dldPosY:
-      format: per_electron
-      group_name: "/uncategorised/FLASH.EXP/HEXTOF.DAQ/DLD1/"
-      slice: 0
-
-    # Detector time-of-flight channel
-    # if split_sector_id_from_dld_time is set to True, This this will generate
-    # also the dldSectorID channel
-    dldTimeSteps:
-      format: per_electron
-      group_name: "/uncategorised/FLASH.EXP/HEXTOF.DAQ/DLD1/"
-      slice: 3
-
-    # The auxiliary channel has a special structure where the group further contains
-    # a multidimensional 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
-
-    # ADC containing the pulser sign (1: value approx. 35000, 0: 33000)
-    pulserSignAdc:
-      format: per_pulse
-      group_name: "/FL1/Experiment/PG/SIS8300 100MHz ADC/CH6/TD/"
-
-    # the energy of the monochromatized beam. This is a quasi-static value.
-    # there is a better channel which still needs implementation.
-    monochromatorPhotonEnergy:
-      format: per_train
-      group_name: "/FL1/Beamlines/PG/Monochromator/monochromator photon energy/"
-
-    # The GMDs can not be read yet...
-    gmdBda:
-      format: per_train
-      group_name: "/FL1/Photon Diagnostic/GMD/Average energy/energy BDA/"
-    # Beam Arrival Monitor, vital for pump-probe experiments as it can compensate sase
-    # timing fluctuations.
-    # Here we use the DBC2 BAM as the "normal" one is broken.
-    bam:
-        format: per_pulse
-        group_name: "/uncategorised/FLASH.SDIAG/BAM.DAQ/FL0.DBC2.ARRIVAL_TIME.ABSOLUTE.SA1.COMP/"
-
-    # The delay Stage position, encoding the pump-probe delay
-    delayStage:
-      format: per_train
-      group_name: "/zraw/FLASH.SYNC/LASER.LOCK.EXP/F1.PG.OSC/FMC0.MD22.1.ENCODER_POSITION.RD/dGroup/"
-
-  # 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_token: <TOKEN>
-
-# The nexus collection routine shall be finalized soon for both instruments
-nexus:
-  reader: "mpes"
-  definition: "NXmpes"
-  input_files: ["../sed/config/NXmpes_config-HEXTOF.json"]
diff --git a/sed/core/logging.py b/sed/core/logging.py
deleted file mode 100644
index c55a8bbb..00000000
--- a/sed/core/logging.py
+++ /dev/null
@@ -1,72 +0,0 @@
-"""
-This module provides a function to set up logging for the application. It configures
-both console and file logging handlers, allowing different log levels for each. The
-log files are stored in a user-specific log directory.
-
-"""
-import logging
-import os
-import sys
-from datetime import datetime
-
-# Default log directory
-DEFAULT_LOG_DIR = os.path.join(os.getcwd(), "logs")
-CONSOLE_VERBOSITY = logging.INFO
-FILE_VERBOSITY = logging.DEBUG
-
-
-def setup_logging(
-    name: str,
-    user_log_path: str = DEFAULT_LOG_DIR,
-) -> logging.Logger:
-    """
-    Configures and returns a logger with specified log levels for console and file handlers.
-
-    Args:
-        name (str): The name of the logger.
-        user_log_path (str): Path to the user-specific log directory. Defaults to DEFAULT_LOG_DIR.
-
-    Returns:
-        logging.Logger: The configured logger instance.
-
-    The logger will always write DEBUG level messages to a file located in the user's log
-    directory, while the console log level can be adjusted based on the 'verbosity' parameter.
-    """
-    # Create logger
-    logger = logging.getLogger(name)
-    if logger.hasHandlers():
-        logger.handlers.clear()
-    logger.setLevel(logging.INFO)  # Set the minimum log level for the logger
-
-    # Create console handler and set level
-    console_handler = logging.StreamHandler(sys.stdout)
-    console_handler.setLevel(CONSOLE_VERBOSITY)
-
-    # Create formatter for console
-    console_formatter = logging.Formatter("%(levelname)s - %(message)s")
-    console_handler.setFormatter(console_formatter)
-
-    # Add console handler to logger
-    logger.addHandler(console_handler)
-
-    # Determine log file path
-    os.makedirs(user_log_path, exist_ok=True)
-    log_file = os.path.join(user_log_path, f"sed_{datetime.now().strftime('%Y-%m-%d')}.log")
-
-    # Create file handler and set level to debug
-    file_handler = logging.FileHandler(log_file)
-    file_handler.setLevel(FILE_VERBOSITY)
-
-    # Create formatter for file
-    file_formatter = logging.Formatter(
-        "%(asctime)s - %(name)s - %(levelname)s - %(message)s in %(filename)s:%(lineno)d",
-    )
-    file_handler.setFormatter(file_formatter)
-
-    # Add file handler to logger
-    logger.addHandler(file_handler)
-
-    # Capture warnings with the logging system
-    logging.captureWarnings(True)
-
-    return logger
diff --git a/sed/loader/flash/loader.py b/sed/loader/flash/loader.py
deleted file mode 100644
index 04d9b051..00000000
--- a/sed/loader/flash/loader.py
+++ /dev/null
@@ -1,935 +0,0 @@
-"""
-This module implements the flash data loader.
-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 functionality.
-"""
-import time
-from functools import reduce
-from pathlib import Path
-from typing import List
-from typing import Sequence
-from typing import Tuple
-from typing import Union
-
-import dask.dataframe as dd
-import h5py
-import numpy as np
-import pyarrow.parquet as pq
-from joblib import delayed
-from joblib import Parallel
-from natsort import natsorted
-from pandas import DataFrame
-from pandas import MultiIndex
-from pandas import Series
-
-from sed.core import dfops
-from sed.loader.base.loader import BaseLoader
-from sed.loader.flash.metadata import MetadataRetriever
-from sed.loader.utils import parse_h5_keys
-from sed.loader.utils import split_dld_time_from_sector_id
-
-
-class FlashLoader(BaseLoader):
-    """
-    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"
-
-    supported_file_types = ["h5"]
-
-    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.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.
-
-        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,
-        excluding pulseId, defined by the json file"""
-        available_channels = list(self._config["dataframe"]["channels"].keys())
-        available_channels.remove("pulseId")
-        return available_channels
-
-    def get_channels(self, formats: Union[str, List[str]] = "", index: bool = False) -> List[str]:
-        """
-        Returns a list of channels associated with the specified format(s).
-
-        Args:
-            formats (Union[str, List[str]]): The desired format(s)
-                                ('per_pulse', 'per_electron', 'per_train', 'all').
-            index (bool): If True, includes channels from the multi_index.
-
-        Returns:
-            List[str]: A list of channels with the specified format(s).
-        """
-        # If 'formats' is a single string, convert it to a list for uniform processing.
-        if isinstance(formats, str):
-            formats = [formats]
-
-        # If 'formats' is a string "all", gather all possible formats.
-        if formats == ["all"]:
-            channels = self.get_channels(["per_pulse", "per_train", "per_electron"], index)
-            return channels
-
-        channels = []
-        for format_ in formats:
-            # Gather channels based on the specified format(s).
-            channels.extend(
-                key
-                for key in self.available_channels
-                if self._config["dataframe"]["channels"][key]["format"] == format_
-                and key != "dldAux"
-            )
-            # Include 'dldAuxChannels' if the format is 'per_pulse'.
-            if format_ == "per_pulse":
-                channels.extend(
-                    self._config["dataframe"]["channels"]["dldAux"]["dldAuxChannels"].keys(),
-                )
-
-        # Include channels from multi_index if 'index' is True.
-        if index:
-            channels.extend(self.multi_index)
-
-        return channels
-
-    def reset_multi_index(self) -> None:
-        """Resets the index per pulse and electron"""
-        self.index_per_electron = None
-        self.index_per_pulse = None
-
-    def create_multi_index_per_electron(self, h5_file: h5py.File) -> None:
-        """
-        Creates an index per electron using pulseId for usage with the electron
-            resolved pandas DataFrame.
-
-        Args:
-            h5_file (h5py.File): The HDF5 file object.
-
-        Notes:
-            - This method relies on the 'pulseId' channel to determine
-                the macrobunch IDs.
-            - It creates a MultiIndex with trainId, pulseId, and electronId
-                as the index levels.
-        """
-
-        # Macrobunch IDs obtained from the pulseId channel
-        [train_id, np_array] = self.create_numpy_array_per_channel(
-            h5_file,
-            "pulseId",
-        )
-
-        # Create a series with the macrobunches as index and
-        # microbunches as values
-        macrobunches = (
-            Series(
-                (np_array[i] for i in train_id.index),
-                name="pulseId",
-                index=train_id,
-            )
-            - self._config["dataframe"]["ubid_offset"]
-        )
-
-        # Explode dataframe to get all microbunch vales per macrobunch,
-        # remove NaN values and convert to type int
-        microbunches = macrobunches.explode().dropna().astype(int)
-
-        # Create temporary index values
-        index_temp = MultiIndex.from_arrays(
-            (microbunches.index, microbunches.values),
-            names=["trainId", "pulseId"],
-        )
-
-        # 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
-        electrons = (
-            Series(
-                [np.arange(electron_counts[i]) for i in range(electron_counts.size)],
-            )
-            .explode()
-            .astype(int)
-        )
-
-        # Create a pandas MultiIndex using the exploded datasets
-        self.index_per_electron = MultiIndex.from_arrays(
-            (microbunches.index, microbunches.values, electrons),
-            names=self.multi_index,
-        )
-
-    def create_multi_index_per_pulse(
-        self,
-        train_id: Series,
-        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.
-
-        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.
-        """
-
-        # Create a pandas MultiIndex, useful for comparing electron and
-        # pulse resolved dataframes
-        self.index_per_pulse = MultiIndex.from_product(
-            (train_id, np.arange(0, np_array.shape[1])),
-            names=["trainId", "pulseId"],
-        )
-
-    def create_numpy_array_per_channel(
-        self,
-        h5_file: h5py.File,
-        channel: str,
-    ) -> Tuple[Series, np.ndarray]:
-        """
-        Returns a numpy array for a given channel name for a given file.
-
-        Args:
-            h5_file (h5py.File): The h5py file object.
-            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.
-
-        """
-        # Get the data from the necessary h5 file and channel
-        group = h5_file[self._config["dataframe"]["channels"][channel]["group_name"]]
-
-        channel_dict = self._config["dataframe"]["channels"][channel]  # channel parameters
-
-        train_id = Series(group["index"], name="trainId")  # macrobunch
-
-        # unpacks the timeStamp or value
-        if channel == "timeStamp":
-            np_array = group["time"][()]
-        else:
-            np_array = group["value"][()]
-
-        # Use predefined axis and slice from the json file
-        # to choose correct dimension for necessary channel
-        if "slice" in channel_dict:
-            np_array = np.take(
-                np_array,
-                channel_dict["slice"],
-                axis=1,
-            )
-        return train_id, np_array
-
-    def create_dataframe_per_electron(
-        self,
-        np_array: np.ndarray,
-        train_id: Series,
-        channel: str,
-    ) -> DataFrame:
-        """
-        Returns a pandas DataFrame for a given channel name of type [per electron].
-
-        Args:
-            np_array (np.ndarray): The numpy array containing the channel data.
-            train_id (Series): The train ID Series.
-            channel (str): The name of the channel.
-
-        Returns:
-            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).
-
-        """
-        return (
-            Series((np_array[i] for i in train_id.index), name=channel)
-            .explode()
-            .dropna()
-            .to_frame()
-            .set_index(self.index_per_electron)
-            .drop(
-                index=np.arange(-self._config["dataframe"]["ubid_offset"], 0),
-                level=1,
-                errors="ignore",
-            )
-        )
-
-    def create_dataframe_per_pulse(
-        self,
-        np_array: np.ndarray,
-        train_id: Series,
-        channel: str,
-        channel_dict: dict,
-    ) -> DataFrame:
-        """
-        Returns a pandas DataFrame for a given channel name of type [per pulse].
-
-        Args:
-            np_array (np.ndarray): The numpy array containing the channel data.
-            train_id (Series): The train ID Series.
-            channel (str): The name of the channel.
-            channel_dict (dict): The dictionary containing channel parameters.
-
-        Returns:
-            DataFrame: The pandas DataFrame for the channel's data.
-
-        Notes:
-            - For auxiliary channels, the macrobunch resolved data is repeated 499 times to be
-              compared to electron resolved data for each auxiliary 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 auxiliary channels
-        if channel == "dldAux":
-            # 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),
-                    name=key,
-                    index=train_id,
-                ).to_frame()
-                for key, value in channel_dict["dldAuxChannels"].items()
-            )
-
-            # Multiindex set and combined dataframe returned
-            data = reduce(DataFrame.combine_first, data_frames)
-
-        # For all other pulse resolved channels
-        else:
-            # 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)
-            data = (
-                Series((np_array[i] for i in train_id.index), name=channel)
-                .explode()
-                .to_frame()
-                .set_index(self.index_per_pulse)
-            )
-
-        return data
-
-    def create_dataframe_per_train(
-        self,
-        np_array: np.ndarray,
-        train_id: Series,
-        channel: str,
-    ) -> DataFrame:
-        """
-        Returns a pandas DataFrame for a given channel name of type [per train].
-
-        Args:
-            np_array (np.ndarray): The numpy array containing the channel data.
-            train_id (Series): The train ID Series.
-            channel (str): The name of the channel.
-
-        Returns:
-            DataFrame: The pandas DataFrame for the channel's data.
-        """
-        return (
-            Series((np_array[i] for i in train_id.index), name=channel)
-            .to_frame()
-            .set_index(train_id)
-        )
-
-    def create_dataframe_per_channel(
-        self,
-        h5_file: h5py.File,
-        channel: str,
-    ) -> Union[Series, DataFrame]:
-        """
-        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.
-
-        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.
-
-        Raises:
-            ValueError: If the channel has an undefined format.
-
-        """
-        [train_id, np_array] = self.create_numpy_array_per_channel(
-            h5_file,
-            channel,
-        )  # numpy Array created
-        channel_dict = self._config["dataframe"]["channels"][channel]  # channel parameters
-
-        # If np_array is size zero, fill with NaNs
-        if np_array.size == 0:
-            # Fill the np_array with NaN values of the same shape as train_id
-            np_array = np.full_like(train_id, np.nan, dtype=np.double)
-            # Create a Series using np_array, with train_id as the index
-            data = Series(
-                (np_array[i] for i in train_id.index),
-                name=channel,
-                index=train_id,
-            )
-
-        # Electron resolved data is treated here
-        if channel_dict["format"] == "per_electron":
-            # If index_per_electron is None, create it for the given file
-            if self.index_per_electron is None:
-                self.create_multi_index_per_electron(h5_file)
-
-            # Create a DataFrame for electron-resolved data
-            data = self.create_dataframe_per_electron(
-                np_array,
-                train_id,
-                channel,
-            )
-
-        # Pulse resolved data is treated here
-        elif channel_dict["format"] == "per_pulse":
-            # Create a DataFrame for pulse-resolved data
-            data = self.create_dataframe_per_pulse(
-                np_array,
-                train_id,
-                channel,
-                channel_dict,
-            )
-
-        # Train resolved data is treated here
-        elif channel_dict["format"] == "per_train":
-            # Create a DataFrame for train-resolved data
-            data = self.create_dataframe_per_train(np_array, train_id, channel)
-
-        else:
-            raise ValueError(
-                channel
-                + "has an undefined format. Available formats are \
-                per_pulse, per_electron and per_train",
-            )
-
-        return data
-
-    def concatenate_channels(
-        self,
-        h5_file: h5py.File,
-    ) -> DataFrame:
-        """
-        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.
-
-        Args:
-            h5_file (h5py.File): The h5py.File object representing the HDF5 file.
-
-        Returns:
-            DataFrame: A concatenated pandas DataFrame containing the channels.
-
-        Raises:
-            ValueError: If the group_name for any channel does not exist in the file.
-
-        """
-        all_keys = parse_h5_keys(h5_file)  # Parses all channels present
-
-        # Check for if the provided group_name actually exists in the file
-        for channel in self._config["dataframe"]["channels"]:
-            if channel == "timeStamp":
-                group_name = self._config["dataframe"]["channels"][channel]["group_name"] + "time"
-            else:
-                group_name = self._config["dataframe"]["channels"][channel]["group_name"] + "value"
-
-            if group_name not in all_keys:
-                raise ValueError(
-                    f"The group_name for channel {channel} does not exist.",
-                )
-
-        # Create a generator expression to generate data frames for each channel
-        data_frames = (
-            self.create_dataframe_per_channel(h5_file, each) for each in self.available_channels
-        )
-
-        # Use the reduce function to join the data frames into a single DataFrame
-        return reduce(
-            lambda left, right: left.join(right, how="outer"),
-            data_frames,
-        )
-
-    def create_dataframe_per_file(
-        self,
-        file_path: Path,
-    ) -> DataFrame:
-        """
-        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.
-
-        Args:
-            file_path (Path): Path to the input HDF5 file.
-
-        Returns:
-            DataFrame: pandas DataFrame
-
-        """
-        # Loads h5 file and creates a dataframe
-        with h5py.File(file_path, "r") as h5_file:
-            self.reset_multi_index()  # Reset MultiIndexes for next file
-            df = self.concatenate_channels(h5_file)
-            df = df.dropna(subset=self._config["dataframe"].get("tof_column", "dldTimeSteps"))
-            # correct the 3 bit shift which encodes the detector ID in the 8s time
-            if self._config["dataframe"].get("split_sector_id_from_dld_time", False):
-                df = split_dld_time_from_sector_id(df, config=self._config)
-            return df
-
-    def create_buffer_file(self, h5_path: Path, parquet_path: Path) -> Union[bool, Exception]:
-        """
-        Converts an HDF5 file to Parquet format to create a buffer 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.
-            parquet_path (Path): Path to the output Parquet file.
-
-        Raises:
-            ValueError: If an error occurs during the conversion process.
-
-        """
-        try:
-            (
-                self.create_dataframe_per_file(h5_path)
-                .reset_index(level=self.multi_index)
-                .to_parquet(parquet_path, index=False)
-            )
-        except Exception as exc:  # pylint: disable=broad-except
-            self.failed_files_error.append(f"{parquet_path}: {type(exc)} {exc}")
-            return exc
-        return None
-
-    def buffer_file_handler(
-        self,
-        data_parquet_dir: Path,
-        detector: str,
-        force_recreate: bool,
-    ) -> Tuple[List[Path], List, List]:
-        """
-        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.
-            force_recreate (bool): Forces recreation of buffer files
-
-        Returns:
-            Tuple[List[Path], List, List]: Three lists, one for
-            parquet file paths, one for metadata and one for schema.
-
-        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
-        ]
-        existing_parquet_filenames = [file for file in parquet_filenames if file.exists()]
-
-        # 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")
-
-        if not force_recreate:
-            # Check if the available channels match the schema of the existing parquet files
-            parquet_schemas = [pq.read_schema(file) for file in existing_parquet_filenames]
-            config_schema = set(self.get_channels(formats="all", index=True))
-            if self._config["dataframe"].get("split_sector_id_from_dld_time", False):
-                config_schema.add(self._config["dataframe"].get("sector_id_column", False))
-
-            for i, schema in enumerate(parquet_schemas):
-                schema_set = set(schema.names)
-                if schema_set != config_schema:
-                    missing_in_parquet = config_schema - schema_set
-                    missing_in_config = schema_set - config_schema
-
-                    missing_in_parquet_str = (
-                        f"Missing in parquet: {missing_in_parquet}" if missing_in_parquet else ""
-                    )
-                    missing_in_config_str = (
-                        f"Missing in config: {missing_in_config}" if missing_in_config else ""
-                    )
-
-                    raise ValueError(
-                        "The available channels do not match the schema of file",
-                        f"{existing_parquet_filenames[i]}",
-                        f"{missing_in_parquet_str}",
-                        f"{missing_in_config_str}",
-                        "Please check the configuration file or set force_recreate to True.",
-                    )
-
-        # Choose files to read
-        files_to_read = [
-            (h5_path, parquet_path)
-            for h5_path, parquet_path in zip(h5_filenames, parquet_filenames)
-            if force_recreate or 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:
-            error = 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
-            )
-            if any(error):
-                raise RuntimeError(f"Conversion failed for some files. {error}")
-
-        # Raise an error if the conversion failed for any files
-        # TODO: merge this and the previous error trackings
-        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!")
-
-        # read all parquet metadata and schema
-        metadata = [pq.read_metadata(file) for file in parquet_filenames]
-        schema = [pq.read_schema(file) for file in parquet_filenames]
-
-        return parquet_filenames, metadata, schema
-
-    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,
-        force_recreate: bool = False,
-    ) -> Tuple[dd.DataFrame, dd.DataFrame]:
-        """
-        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.
-            force_recreate (bool, optional): Forces recreation of buffer file.
-        Returns:
-            tuple: A tuple containing two dataframes:
-            - dataframe_electron: Dataframe containing the loaded/augmented electron data.
-            - dataframe_pulse: Dataframe containing the loaded/augmented timed 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_electron = dd.read_parquet(parquet_path)
-                dataframe_pulse = dataframe_electron
-            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 parquet filenames, metadata and schema from the method
-            # which handles buffer file creation/reading
-            filenames, metadata, _ = self.buffer_file_handler(
-                data_parquet_dir,
-                detector,
-                force_recreate,
-            )
-
-            # Read all parquet files into one dataframe using dask
-            dataframe = dd.read_parquet(filenames, calculate_divisions=True)
-
-            # Channels to fill NaN values
-            channels: List[str] = self.get_channels(["per_pulse", "per_train"])
-
-            overlap = min(file.num_rows for file in metadata)
-
-            print("Filling nan values...")
-            dataframe = dfops.forward_fill_lazy(
-                df=dataframe,
-                columns=channels,
-                before=overlap,
-                iterations=self._config["dataframe"].get("forward_fill_iterations", 2),
-            )
-            # Remove the NaNs from per_electron channels
-            dataframe_electron = dataframe.dropna(
-                subset=self.get_channels(["per_electron"]),
-            )
-            dataframe_pulse = dataframe[
-                self.multi_index + self.get_channels(["per_pulse", "per_train"])
-            ]
-            dataframe_pulse = dataframe_pulse[
-                (dataframe_pulse["electronId"] == 0) | (np.isnan(dataframe_pulse["electronId"]))
-            ]
-
-        # Save the dataframe as parquet if requested
-        if save_parquet:
-            dataframe_electron.compute().reset_index(drop=True).to_parquet(parquet_path)
-            print("Combined parquet file saved.")
-
-        return dataframe_electron, dataframe_pulse
-
-    def parse_metadata(self, scicat_token: str = None) -> dict:
-        """Uses the MetadataRetriever class to fetch metadata from scicat for each run.
-
-        Returns:
-            dict: Metadata dictionary
-            scicat_token (str, optional):: The scicat token to use for fetching metadata
-        """
-        metadata_retriever = MetadataRetriever(self._config["metadata"], scicat_token)
-        metadata = metadata_retriever.get_metadata(
-            beamtime_id=self._config["core"]["beamtime_id"],
-            runs=self.runs,
-            metadata=self.metadata,
-        )
-
-        return metadata
-
-    def get_count_rate(
-        self,
-        fids: Sequence[int] = None,  # noqa: ARG002
-        **kwds,  # noqa: ARG002
-    ):
-        return None, None
-
-    def get_elapsed_time(self, fids=None, **kwds):  # noqa: ARG002
-        return None
-
-    def read_dataframe(
-        self,
-        files: Union[str, Sequence[str]] = None,
-        folders: Union[str, Sequence[str]] = None,
-        runs: Union[str, Sequence[str]] = None,
-        ftype: str = "h5",
-        metadata: dict = None,
-        collect_metadata: bool = False,
-        **kwds,
-    ) -> Tuple[dd.DataFrame, dd.DataFrame, dict]:
-        """
-        Read express data from the DAQ, generating a parquet in between.
-
-        Args:
-            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.
-            runs (Union[str, Sequence[str]], optional): Run identifier(s). Corresponding files will
-                be located in the location provided by ``folders``. Takes precedence 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.
-
-        Returns:
-            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.
-        """
-        t0 = time.time()
-
-        data_raw_dir, data_parquet_dir = self.initialize_paths()
-
-        # 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)):
-                runs = [runs]
-            for run in runs:
-                run_files = self.get_files_from_run_id(
-                    run_id=run,
-                    folders=[str(folder.resolve()) for folder in data_raw_dir],
-                    extension=ftype,
-                    daq=self._config["dataframe"]["daq"],
-                )
-                files.extend(run_files)
-            self.runs = list(runs)
-            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.
-            super().read_dataframe(
-                files=files,
-                folders=folders,
-                ftype=ftype,
-                metadata=metadata,
-            )
-
-        df, df_timed = self.parquet_handler(data_parquet_dir, **kwds)
-
-        metadata = self.parse_metadata(**kwds) if collect_metadata else {}
-        print(f"loading complete in {time.time() - t0: .2f} s")
-
-        return df, df_timed, metadata
-
-
-LOADER = FlashLoader
diff --git a/sed/__init__.py b/src/sed/__init__.py
similarity index 54%
rename from sed/__init__.py
rename to src/sed/__init__.py
index 50850279..3d03ef82 100644
--- a/sed/__init__.py
+++ b/src/sed/__init__.py
@@ -1,7 +1,11 @@
 """sed module easy access APIs."""
 import importlib.metadata
 
-from .core.processor import SedProcessor
+import dask
+
+dask.config.set({"dataframe.query-planning": False})
+
+from .core.processor import SedProcessor  # noqa: E402
 
 __version__ = importlib.metadata.version("sed-processor")
 __all__ = ["SedProcessor"]
diff --git a/sed/binning/__init__.py b/src/sed/binning/__init__.py
similarity index 100%
rename from sed/binning/__init__.py
rename to src/sed/binning/__init__.py
diff --git a/sed/binning/binning.py b/src/sed/binning/binning.py
similarity index 90%
rename from sed/binning/binning.py
rename to src/sed/binning/binning.py
index 28daeb80..00103a8f 100644
--- a/sed/binning/binning.py
+++ b/src/sed/binning/binning.py
@@ -1,12 +1,11 @@
 """This module contains the binning functions of the sed.binning module
-
 """
+from __future__ import annotations
+
 import gc
+from collections.abc import Sequence
 from functools import reduce
 from typing import cast
-from typing import List
-from typing import Sequence
-from typing import Tuple
 from typing import Union
 
 import dask.dataframe
@@ -26,27 +25,21 @@
 
 
 def bin_partition(
-    part: Union[dask.dataframe.DataFrame, pd.DataFrame],
-    bins: Union[
-        int,
-        dict,
-        Sequence[int],
-        Sequence[np.ndarray],
-        Sequence[tuple],
-    ] = 100,
+    part: dask.dataframe.DataFrame | pd.DataFrame,
+    bins: int | dict | Sequence[int] | Sequence[np.ndarray] | Sequence[tuple] = 100,
     axes: Sequence[str] = None,
-    ranges: Sequence[Tuple[float, float]] = None,
+    ranges: Sequence[tuple[float, float]] = None,
     hist_mode: str = "numba",
-    jitter: Union[list, dict] = None,
+    jitter: list | dict = None,
     return_edges: bool = False,
     skip_test: bool = False,
-) -> Union[np.ndarray, Tuple[np.ndarray, list]]:
+) -> np.ndarray | tuple[np.ndarray, list]:
     """Compute the n-dimensional histogram of a single dataframe partition.
 
     Args:
-        part (Union[dask.dataframe.DataFrame, pd.DataFrame]): dataframe on which
+        part (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
@@ -70,7 +63,7 @@ def bin_partition(
             the order of the dimensions in the resulting array. Only not required if
             bins are provided as dictionary containing the axis names.
             Defaults to None.
-        ranges (Sequence[Tuple[float, float]], optional): Sequence of tuples containing
+        ranges (Sequence[tuple[float, float]], optional): Sequence of tuples containing
             the start and end point of the binning range. Required if bins given as
             int or Sequence[int]. Defaults to None.
         hist_mode (str, optional): Histogram calculation method.
@@ -79,7 +72,7 @@ def bin_partition(
                 - "numba" use a numba powered similar method.
 
             Defaults to "numba".
-        jitter (Union[list, dict], optional): a list of the axes on which to apply
+        jitter (list | dict, optional): a list of the axes on which to apply
             jittering. To specify the jitter amplitude or method (normal or uniform
             noise) a dictionary can be passed. This should look like
             jitter={'axis':{'amplitude':0.5,'mode':'uniform'}}.
@@ -102,8 +95,8 @@ def bin_partition(
             present in the dataframe
 
     Returns:
-        Union[np.ndarray, Tuple[np.ndarray, list]]: 2-element tuple returned only when
-        returnEdges is True. Otherwise only hist is returned.
+        np.ndarray | tuple[np.ndarray: 2-element tuple returned only when
+        return_edges is True. Otherwise only hist is returned.
 
         - **hist**: The result of the n-dimensional binning
         - **edges**: A list of D arrays describing the bin edges for each dimension.
@@ -122,17 +115,17 @@ def bin_partition(
             raise TypeError(
                 "axes needs to be of type 'List[str]' if tests are skipped!",
             )
-        bins = cast(Union[List[int], List[np.ndarray]], bins)
-        axes = cast(List[str], axes)
-        ranges = cast(List[Tuple[float, float]], ranges)
+        bins = cast(Union[list[int], list[np.ndarray]], bins)
+        axes = cast(list[str], axes)
+        ranges = cast(list[tuple[float, float]], ranges)
 
     # convert bin centers to bin edges:
     if all(isinstance(x, np.ndarray) for x in bins):
-        bins = cast(List[np.ndarray], bins)
+        bins = cast(list[np.ndarray], bins)
         for i, bin_centers in enumerate(bins):
             bins[i] = bin_centers_to_bin_edges(bin_centers)
     else:
-        bins = cast(List[int], bins)
+        bins = cast(list[int], bins)
         # shift ranges by half a bin size to align the bin centers to the given ranges,
         # as the histogram functions interpret the ranges as limits for the edges.
         for i, nbins in enumerate(bins):
@@ -203,24 +196,18 @@ def bin_partition(
 
 def bin_dataframe(
     df: dask.dataframe.DataFrame,
-    bins: Union[
-        int,
-        dict,
-        Sequence[int],
-        Sequence[np.ndarray],
-        Sequence[tuple],
-    ] = 100,
+    bins: int | dict | Sequence[int] | Sequence[np.ndarray] | Sequence[tuple] = 100,
     axes: Sequence[str] = None,
-    ranges: Sequence[Tuple[float, float]] = None,
+    ranges: Sequence[tuple[float, float]] = None,
     hist_mode: str = "numba",
     mode: str = "fast",
-    jitter: Union[list, dict] = None,
+    jitter: list | dict = None,
     pbar: bool = True,
     n_cores: int = N_CPU - 1,
     threads_per_worker: int = 4,
     threadpool_api: str = "blas",
     return_partitions: bool = False,
-    **kwds,
+    compute_kwds: dict = {},
 ) -> xr.DataArray:
     """Computes the n-dimensional histogram on columns of a dataframe,
     parallelized.
@@ -228,7 +215,7 @@ def bin_dataframe(
     Args:
         df (dask.dataframe.DataFrame): a dask.DataFrame on which to perform the
             histogram.
-            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
@@ -252,7 +239,7 @@ def bin_dataframe(
             the order of the dimensions in the resulting array. Only not required if
             bins are provided as dictionary containing the axis names.
             Defaults to None.
-        ranges (Sequence[Tuple[float, float]], optional): Sequence of tuples containing
+        ranges (Sequence[tuple[float, float]], optional): Sequence of tuples containing
             the start and end point of the binning range. Required if bins given as
             int or Sequence[int]. Defaults to None.
         hist_mode (str, optional): Histogram calculation method.
@@ -269,7 +256,7 @@ def bin_dataframe(
                 - 'legacy': Single-core recombination of partition results.
 
             Defaults to "fast".
-        jitter (Union[list, dict], optional): a list of the axes on which to apply
+        jitter (list | dict, optional): a list of the axes on which to apply
             jittering. To specify the jitter amplitude or method (normal or uniform
             noise) a dictionary can be passed. This should look like
             jitter={'axis':{'amplitude':0.5,'mode':'uniform'}}.
@@ -280,7 +267,7 @@ def bin_dataframe(
             Defaults to None.
         pbar (bool, optional): Option to show the tqdm progress bar. Defaults to True.
         n_cores (int, optional): Number of CPU cores to use for parallelization.
-            Defaults to all but one of the available cores. Defaults to N_CPU-1.
+            Defaults to all but one of the available cores.
         threads_per_worker (int, optional): Limit the number of threads that
             multiprocessing can spawn. Defaults to 4.
         threadpool_api (str, optional): The API to use for multiprocessing.
@@ -288,7 +275,7 @@ def bin_dataframe(
         return_partitions (bool, optional): Option to return a hypercube of dimension
             n+1, where the last dimension corresponds to the dataframe partitions.
             Defaults to False.
-        **kwds: Keyword arguments passed to ``dask.compute()``
+        compute_kwds (dict, optional): Dict of Keyword arguments passed to ``dask.compute()``
 
     Raises:
         Warning: Warns if there are unimplemented features the user is trying to use.
@@ -304,14 +291,14 @@ def bin_dataframe(
     # create the coordinate axes for the xarray output
     # if provided as array, they are interpreted as bin centers
     if isinstance(bins[0], np.ndarray):
-        bins = cast(List[np.ndarray], bins)
+        bins = cast(list[np.ndarray], bins)
         coords = dict(zip(axes, bins))
     elif ranges is None:
         raise ValueError(
             "bins is not an array and range is none. this shouldn't happen.",
         )
     else:
-        bins = cast(List[int], bins)
+        bins = cast(list[int], bins)
         coords = {
             ax: np.linspace(r[0], r[1], n, endpoint=False) for ax, r, n in zip(axes, ranges, bins)
         }
@@ -348,7 +335,7 @@ def bin_dataframe(
                 )
 
             if len(core_tasks) > 0:
-                core_results = dask.compute(*core_tasks, **kwds)
+                core_results = dask.compute(*core_tasks, **compute_kwds)
 
                 if return_partitions:
                     for core_result in core_results:
@@ -389,7 +376,7 @@ def bin_dataframe(
                         combine_tasks.append(
                             dask.delayed(reduce)(_arraysum, combine_parts),
                         )
-                    combine_results = dask.compute(*combine_tasks, **kwds)
+                    combine_results = dask.compute(*combine_tasks, **compute_kwds)
                     # Directly fill into target array. This is much faster than
                     # the (not so parallel) reduce/concatenation used before,
                     # and uses less memory.
@@ -509,7 +496,7 @@ def normalization_histogram_from_timed_dataframe(
 
 
 def apply_jitter_on_column(
-    df: Union[dask.dataframe.core.DataFrame, pd.DataFrame],
+    df: dask.dataframe.core.DataFrame | pd.DataFrame,
     amp: float,
     col: str,
     mode: str = "uniform",
diff --git a/sed/binning/numba_bin.py b/src/sed/binning/numba_bin.py
similarity index 93%
rename from sed/binning/numba_bin.py
rename to src/sed/binning/numba_bin.py
index d62986d4..7daeda97 100644
--- a/sed/binning/numba_bin.py
+++ b/src/sed/binning/numba_bin.py
@@ -1,13 +1,11 @@
 """This file contains code for binning using numba precompiled code for the
 sed.binning module
-
 """
+from __future__ import annotations
+
+from collections.abc import Sequence
 from typing import Any
 from typing import cast
-from typing import List
-from typing import Sequence
-from typing import Tuple
-from typing import Union
 
 import numba
 import numpy as np
@@ -108,7 +106,7 @@ def binsearch(bins: np.ndarray, val: float) -> int:
 def _hist_from_bins(
     sample: np.ndarray,
     bins: Sequence[np.ndarray],
-    shape: Tuple,
+    shape: tuple,
 ) -> np.ndarray:
     """Numba powered binning method, similar to np.histogramdd.
 
@@ -118,7 +116,7 @@ def _hist_from_bins(
         sample (np.ndarray) : the array of shape (N,D) on which to compute the histogram
         bins (Sequence[np.ndarray]): array of shape (N,D) defining the D bins on which
             to compute the histogram, i.e. the desired output axes.
-        shape (Tuple): shape of the resulting array. Workaround for the fact numba
+        shape (tuple): shape of the resulting array. Workaround for the fact numba
             does not allow to create tuples.
     Returns:
         hist: the computed n-dimensional histogram
@@ -154,9 +152,9 @@ def _hist_from_bins(
 
 def numba_histogramdd(
     sample: np.ndarray,
-    bins: Union[int, Sequence[int], Sequence[np.ndarray], np.ndarray],
+    bins: int | Sequence[int] | Sequence[np.ndarray] | np.ndarray,
     ranges: Sequence = None,
-) -> Tuple[np.ndarray, List[np.ndarray]]:
+) -> tuple[np.ndarray, list[np.ndarray]]:
     """Multidimensional histogram function, powered by Numba.
 
     Behaves in total much like numpy.histogramdd. Returns uint32 arrays.
@@ -168,7 +166,7 @@ def numba_histogramdd(
 
     Args:
         sample (np.ndarray): The data to be histogram'd with shape N,D
-        bins (Union[int, Sequence[int], Sequence[np.ndarray], np.ndarray]): The number
+        bins (int | Sequence[int] | Sequence[np.ndarray] | np.ndarray): The number
             of bins for each dimension D, or a sequence of bin edges on which to calculate
             the histogram.
         ranges (Sequence, optional): The range(s) to use for binning when bins is a sequence
@@ -181,7 +179,7 @@ def numba_histogramdd(
         RuntimeError: Internal shape error after binning
 
     Returns:
-        Tuple[np.ndarray, List[np.ndarray]]: 2-element tuple of The computed histogram
+        tuple[np.ndarray, list[np.ndarray]]: 2-element tuple of The computed histogram
         and s list of D arrays describing the bin edges for each dimension.
 
         - **hist**: The computed histogram
@@ -213,7 +211,7 @@ def numba_histogramdd(
 
     # method == "array"
     if isinstance(bins[0], np.ndarray):
-        bins = cast(List[np.ndarray], list(bins))
+        bins = cast(list[np.ndarray], list(bins))
         hist = _hist_from_bins(
             sample,
             tuple(bins),
@@ -239,7 +237,7 @@ def numba_histogramdd(
     bins = tuple(bins)
 
     # Create edge arrays
-    edges: List[Any] = []
+    edges: list[Any] = []
     nbin = np.empty(num_cols, int)
 
     for i in range(num_cols):
diff --git a/sed/binning/utils.py b/src/sed/binning/utils.py
similarity index 88%
rename from sed/binning/utils.py
rename to src/sed/binning/utils.py
index a106e2c7..d5b387ad 100644
--- a/sed/binning/utils.py
+++ b/src/sed/binning/utils.py
@@ -1,11 +1,9 @@
 """This file contains helper functions for the sed.binning module
-
 """
+from __future__ import annotations
+
+from collections.abc import Sequence
 from typing import cast
-from typing import List
-from typing import Sequence
-from typing import Tuple
-from typing import Union
 
 import numpy as np
 
@@ -16,16 +14,10 @@ def _arraysum(array_a, array_b):
 
 
 def simplify_binning_arguments(
-    bins: Union[
-        int,
-        dict,
-        Sequence[int],
-        Sequence[np.ndarray],
-        Sequence[tuple],
-    ],
+    bins: int | dict | Sequence[int] | Sequence[np.ndarray] | Sequence[tuple],
     axes: Sequence[str] = None,
-    ranges: Sequence[Tuple[float, float]] = None,
-) -> Tuple[Union[List[int], List[np.ndarray]], List[str], List[Tuple[float, float]]]:
+    ranges: Sequence[tuple[float, float]] = None,
+) -> tuple[list[int] | list[np.ndarray], list[str], list[tuple[float, float]]]:
     """Convert the flexible input for defining bins into a
     simple "axes" "bins" "ranges" tuple.
 
@@ -33,7 +25,7 @@ def simplify_binning_arguments(
     binning functions defined here.
 
     Args:
-        bins (int, dict, Sequence[int], Sequence[np.ndarray], Sequence[tuple]):
+        bins (int | dict | Sequence[int] | Sequence[np.ndarray] | Sequence[tuple]):
             Definition of the bins. Can  be any of the following cases:
 
                 - an integer describing the number of bins for all dimensions. This
@@ -56,7 +48,7 @@ def simplify_binning_arguments(
             the order of the dimensions in the resulting array. Only not required if
             bins are provided as dictionary containing the axis names.
             Defaults to None.
-        ranges (Sequence[Tuple[float, float]], optional): Sequence of tuples containing
+        ranges (Sequence[tuple[float, float]], optional): Sequence of tuples containing
             the start and end point of the binning range. Required if bins given as
             int or Sequence[int]. Defaults to None.
 
@@ -67,7 +59,7 @@ def simplify_binning_arguments(
         AttributeError: Shape mismatch
 
     Returns:
-        Tuple[Union[List[int], List[np.ndarray]], List[Tuple[float, float]]]: Tuple
+        tuple[list[int] | list[np.ndarray], list[str], list[tuple[float, float]]]: Tuple
         containing lists of bin centers, axes, and ranges.
     """
     # if bins is a dictionary: unravel to axes and bins
@@ -113,7 +105,7 @@ def simplify_binning_arguments(
 
     # if bins are provided as int, check that ranges are present
     if all(isinstance(x, (int, np.int64)) for x in bins):
-        bins = cast(List[int], list(bins))
+        bins = cast(list[int], list(bins))
         if ranges is None:
             raise AttributeError(
                 "Must provide a range if bins is an integer or list of integers",
@@ -125,7 +117,7 @@ def simplify_binning_arguments(
 
     # otherwise, all bins should be of type np.ndarray here
     elif all(isinstance(x, np.ndarray) for x in bins):
-        bins = cast(List[np.ndarray], list(bins))
+        bins = cast(list[np.ndarray], list(bins))
     else:
         raise TypeError(f"Could not interpret bins of type {type(bins)}")
 
diff --git a/sed/calibrator/__init__.py b/src/sed/calibrator/__init__.py
similarity index 100%
rename from sed/calibrator/__init__.py
rename to src/sed/calibrator/__init__.py
diff --git a/sed/calibrator/delay.py b/src/sed/calibrator/delay.py
similarity index 65%
rename from sed/calibrator/delay.py
rename to src/sed/calibrator/delay.py
index c983c6ba..14758b41 100644
--- a/sed/calibrator/delay.py
+++ b/src/sed/calibrator/delay.py
@@ -1,13 +1,11 @@
 """sed.calibrator.delay module. Code for delay calibration.
 """
+from __future__ import annotations
+
+from collections.abc import Sequence
 from copy import deepcopy
 from datetime import datetime
 from typing import Any
-from typing import Dict
-from typing import List
-from typing import Sequence
-from typing import Tuple
-from typing import Union
 
 import dask.dataframe
 import h5py
@@ -15,6 +13,11 @@
 import pandas as pd
 
 from sed.core import dfops
+from sed.core.logging import set_verbosity
+from sed.core.logging import setup_logging
+
+# Configure logging
+logger = setup_logging("delay")
 
 
 class DelayCalibrator:
@@ -24,67 +27,94 @@ class DelayCalibrator:
 
     Args:
         config (dict, optional): Config dictionary. Defaults to None.
+        verbose (bool, optional): Option to print out diagnostic information.
+            Defaults to True.
     """
 
     def __init__(
         self,
         config: dict = None,
+        verbose: bool = True,
     ) -> None:
         """Initialization of the DelayCalibrator class passes the config.
 
         Args:
             config (dict, optional): Config dictionary. Defaults to None.
+            verbose (bool, optional): Option to print out diagnostic information.
+                Defaults to True.
         """
         if config is not None:
             self._config = config
         else:
             self._config = {}
 
-        self.adc_column: str = self._config["dataframe"].get("adc_column", None)
-        self.delay_column: str = self._config["dataframe"]["delay_column"]
-        self.corrected_delay_column = self._config["dataframe"].get(
-            "corrected_delay_column",
+        self._verbose = verbose
+        set_verbosity(logger, self._verbose)
+
+        self.adc_column: str = config["dataframe"]["columns"]["adc"]
+        self.delay_column: str = config["dataframe"]["columns"]["delay"]
+        self.corrected_delay_column = self._config["dataframe"]["columns"].get(
+            "corrected_delay",
             self.delay_column,
         )
-        self.calibration: Dict[str, Any] = self._config["delay"].get("calibration", {})
-        self.offsets: Dict[str, Any] = self._config["delay"].get("offsets", {})
+        self.calibration: dict[str, Any] = self._config["delay"].get("calibration", {})
+        self.offsets: dict[str, Any] = self._config["delay"].get("offsets", {})
+
+    @property
+    def verbose(self) -> bool:
+        """Accessor to the verbosity flag.
+
+        Returns:
+            bool: Verbosity flag.
+        """
+        return self._verbose
+
+    @verbose.setter
+    def verbose(self, verbose: bool):
+        """Setter for the verbosity.
+
+        Args:
+            verbose (bool): Option to turn on verbose output. Sets loglevel to INFO.
+        """
+        self._verbose = verbose
+        set_verbosity(logger, self._verbose)
 
     def append_delay_axis(
         self,
-        df: Union[pd.DataFrame, dask.dataframe.DataFrame],
+        df: pd.DataFrame | dask.dataframe.DataFrame,
         adc_column: str = None,
         delay_column: str = None,
-        calibration: Dict[str, Any] = None,
-        adc_range: Union[Tuple, List, np.ndarray] = None,
-        delay_range: Union[Tuple, List, np.ndarray] = None,
+        calibration: dict[str, Any] = None,
+        adc_range: tuple | list | np.ndarray = None,
+        delay_range: tuple | list | np.ndarray = None,
         time0: float = None,
-        delay_range_mm: Union[Tuple, List, np.ndarray] = None,
+        delay_range_mm: tuple | list | np.ndarray = None,
         datafile: str = None,
         p1_key: str = None,
         p2_key: str = None,
         t0_key: str = None,
-        verbose: bool = True,
-    ) -> Tuple[Union[pd.DataFrame, dask.dataframe.DataFrame], dict]:
+        suppress_output: bool = False,
+    ) -> tuple[pd.DataFrame | dask.dataframe.DataFrame, dict]:
         """Calculate and append the delay axis to the events dataframe, by converting
         values from an analog-digital-converter (ADC).
 
         Args:
-            df (Union[pd.DataFrame, dask.dataframe.DataFrame]): The dataframe where
+            df (pd.DataFrame | dask.dataframe.DataFrame): The dataframe where
                 to apply the delay calibration to.
             adc_column (str, optional): Source column for delay calibration.
-                Defaults to config["dataframe"]["adc_column"].
+                Defaults to config["dataframe"]["columns"]["adc"].
             delay_column (str, optional): Destination column for delay calibration.
-                Defaults to config["dataframe"]["delay_column"].
+                Defaults to config["dataframe"]["columns"]["delay"].
             calibration (dict, optional): Calibration dictionary with parameters for
                 delay calibration.
-            adc_range (Union[Tuple, List, np.ndarray], optional): The range of used
+            adc_range (tuple | list | np.ndarray, optional): The range of used
                 ADC values. Defaults to config["delay"]["adc_range"].
-            delay_range (Union[Tuple, List, np.ndarray], optional): Range of scanned
+            delay_range (tuple | list | np.ndarray, optional): Range of scanned
                 delay values in ps. If omitted, the range is calculated from the
                 delay_range_mm and t0 values.
             time0 (float, optional): Pump-Probe overlap value of the delay coordinate.
                 If omitted, it is searched for in the data files.
-            delay_range_mm (Union[Tuple, List, np.ndarray], optional): Range of scanned
+            delay_range_mm (tuple | list | np.ndarray, optional): Range of scanned
                 delay stage in mm. If omitted, it is searched for in the data files.
             datafile (str, optional): Datafile in which delay parameters are searched
                 for. Defaults to None.
@@ -94,15 +124,14 @@ def append_delay_axis(
                 Defaults to config["delay"]["p2_key"]
             t0_key (str, optional): hdf5 key for t0 value (mm).
                 Defaults to config["delay"]["t0_key"]
-            verbose (bool, optional): Option to print out diagnostic information.
-                Defaults to True.
+            suppress_output (bool, optional): Option to suppress log output. Defaults to False.
 
         Raises:
             ValueError: Raised if delay parameters are not found in the file.
             NotImplementedError: Raised if no sufficient information passed.
 
         Returns:
-            Union[pd.DataFrame, dask.dataframe.DataFrame]: dataframe with added column
+            tuple[pd.DataFrame | dask.dataframe.DataFrame, dict]: dataframe with added column
             and delay calibration metadata dictionary.
         """
         # pylint: disable=duplicate-code
@@ -117,7 +146,7 @@ def append_delay_axis(
             or datafile is not None
         ):
             calibration = {}
-            calibration["creation_date"] = datetime.now().timestamp()
+            calibration["creation_date"] = datetime.now()
             if adc_range is not None:
                 calibration["adc_range"] = adc_range
             if delay_range is not None:
@@ -128,11 +157,9 @@ def append_delay_axis(
                 calibration["delay_range_mm"] = delay_range_mm
         else:
             # report usage of loaded parameters
-            if "creation_date" in calibration and verbose:
-                datestring = datetime.fromtimestamp(calibration["creation_date"]).strftime(
-                    "%m/%d/%Y, %H:%M:%S",
-                )
-                print(f"Using delay calibration parameters generated on {datestring}")
+            if "creation_date" in calibration and not suppress_output:
+                datestring = calibration["creation_date"].strftime("%m/%d/%Y, %H:%M:%S")
+                logger.info(f"Using delay calibration parameters generated on {datestring}")
 
         if adc_column is None:
             adc_column = self.adc_column
@@ -146,9 +173,10 @@ def append_delay_axis(
             t0_key = self._config["delay"].get("t0_key", "")
 
         if "adc_range" not in calibration.keys():
-            calibration["adc_range"] = np.asarray(
-                self._config["delay"]["adc_range"],
-            ) / 2 ** (self._config["dataframe"]["adc_binning"] - 1)
+            calibration["adc_range"] = (
+                np.asarray(self._config["delay"]["adc_range"])
+                / self._config["dataframe"]["adc_binning"]
+            )
 
         if "delay_range" not in calibration.keys():
             if "delay_range_mm" not in calibration.keys() or "time0" not in calibration.keys():
@@ -167,8 +195,8 @@ def append_delay_axis(
                     calibration["datafile"] = datafile
                     calibration["delay_range_mm"] = (ret[0], ret[1])
                     calibration["time0"] = ret[2]
-                    if verbose:
-                        print(f"Extract delay range from file '{datafile}'.")
+                    if not suppress_output:
+                        logger.info(f"Extract delay range from file '{datafile}'.")
                 else:
                     raise NotImplementedError(
                         "Not enough parameters for delay calibration.",
@@ -180,9 +208,9 @@ def append_delay_axis(
                     calibration["time0"],
                 ),
             )
-            if verbose:
-                print(f"Converted delay_range (ps) = {calibration['delay_range']}")
-            calibration["creation_date"] = datetime.now().timestamp()
+            if not suppress_output:
+                logger.info(f"Converted delay_range (ps) = {calibration['delay_range']}")
+            calibration["creation_date"] = datetime.now()
 
         if "delay_range" in calibration.keys():
             df[delay_column] = calibration["delay_range"][0] + (
@@ -191,8 +219,8 @@ def append_delay_axis(
                 calibration["adc_range"][1] - calibration["adc_range"][0]
             )
             self.calibration = deepcopy(calibration)
-            if verbose:
-                print(
+            if not suppress_output:
+                logger.info(
                     "Append delay axis using delay_range = "
                     f"[{calibration['delay_range'][0]}, {calibration['delay_range'][1]}]"
                     " and adc_range = "
@@ -207,39 +235,39 @@ def append_delay_axis(
     def add_offsets(
         self,
         df: dask.dataframe.DataFrame,
-        offsets: Dict[str, Any] = None,
+        offsets: dict[str, Any] = None,
         constant: float = None,
         flip_delay_axis: bool = None,
-        columns: Union[str, Sequence[str]] = None,
-        weights: Union[float, Sequence[float]] = 1.0,
-        preserve_mean: Union[bool, Sequence[bool]] = False,
-        reductions: Union[str, Sequence[str]] = None,
+        columns: str | Sequence[str] = None,
+        weights: float | Sequence[float] = 1.0,
+        preserve_mean: bool | Sequence[bool] = False,
+        reductions: str | Sequence[str] = None,
         delay_column: str = None,
-        verbose: bool = True,
-    ) -> Tuple[dask.dataframe.DataFrame, dict]:
+        suppress_output: bool = False,
+    ) -> tuple[dask.dataframe.DataFrame, dict]:
         """Apply an offset to the delay column based on a constant or other columns.
 
         Args:
             df (Union[pd.DataFrame, dask.dataframe.DataFrame]): Dataframe to use.
-            offsets (Dict, optional): Dictionary of delay offset parameters.
+            offsets (dict, optional): Dictionary of delay offset parameters.
             constant (float, optional): The constant to shift the delay axis by.
             flip_delay_axis (bool, optional): Whether to flip the time axis. Defaults to False.
-            columns (Union[str, Sequence[str]]): Name of the column(s) to apply the shift from.
-            weights (Union[int, Sequence[int]]): weights to apply to the columns.
+            columns (str | Sequence[str]): Name of the column(s) to apply the shift from.
+            weights (float | Sequence[float]): weights to apply to the columns.
                 Can also be used to flip the sign (e.g. -1). Defaults to 1.
-            preserve_mean (bool): Whether to subtract the mean of the column before applying the
-                shift. Defaults to False.
-            reductions (str): The reduction to apply to the column. Should be an available method
-                of dask.dataframe.Series. For example "mean". In this case the function is applied
-                to the column to generate a single value for the whole dataset. If None, the shift
-                is applied per-dataframe-row. Defaults to None. Currently only "mean" is supported.
+            preserve_mean (bool | Sequence[bool]): Whether to subtract the mean of the column
+                before applying the shift. Defaults to False.
+            reductions (str | Sequence[str]): The reduction to apply to the column. Should be an
+                available method of dask.dataframe.Series. For example "mean". In this case the
+                function is applied to the column to generate a single value for the whole dataset.
+                If None, the shift is applied per-dataframe-row. Defaults to None. Currently only
+                "mean" is supported.
             delay_column (str, optional): Name of the column containing the delay values.
-            verbose (bool, optional): Option to print out diagnostic information.
-                Defaults to True.
+            suppress_output (bool, optional): Option to suppress log output. Defaults to False.
 
         Returns:
-            dask.dataframe.DataFrame: Dataframe with the shifted delay axis.
-            dict: Metadata dictionary.
+            tuple[dask.dataframe.DataFrame, dict]: Dataframe with the shifted delay axis and
+            Metadata dictionary.
         """
         if offsets is None:
             offsets = deepcopy(self.offsets)
@@ -247,7 +275,7 @@ def add_offsets(
         if delay_column is None:
             delay_column = self.delay_column
 
-        metadata: Dict[str, Any] = {
+        metadata: dict[str, Any] = {
             "applied": True,
         }
 
@@ -255,9 +283,10 @@ def add_offsets(
             # pylint:disable=duplicate-code
             # use passed parameters, overwrite config
             offsets = {}
-            offsets["creation_date"] = datetime.now().timestamp()
+            offsets["creation_date"] = datetime.now()
             # column-based offsets
             if columns is not None:
+                offsets["columns"] = {}
                 if weights is None:
                     weights = 1
                 if isinstance(weights, (int, float, np.integer, np.floating)):
@@ -284,7 +313,7 @@ def add_offsets(
 
                 # store in offsets dictionary
                 for col, weight, pmean, red in zip(columns, weights, preserve_mean, reductions):
-                    offsets[col] = {
+                    offsets["columns"][col] = {
                         "weight": weight,
                         "preserve_mean": pmean,
                         "reduction": red,
@@ -299,11 +328,9 @@ def add_offsets(
             if flip_delay_axis:
                 offsets["flip_delay_axis"] = flip_delay_axis
 
-        elif "creation_date" in offsets and verbose:
-            datestring = datetime.fromtimestamp(offsets["creation_date"]).strftime(
-                "%m/%d/%Y, %H:%M:%S",
-            )
-            print(f"Using delay offset parameters generated on {datestring}")
+        elif "creation_date" in offsets and not suppress_output:
+            datestring = offsets["creation_date"].strftime("%m/%d/%Y, %H:%M:%S")
+            logger.info(f"Using delay offset parameters generated on {datestring}")
 
         if len(offsets) > 0:
             # unpack dictionary
@@ -311,15 +338,13 @@ def add_offsets(
             weights = []
             preserve_mean = []
             reductions = []
-            if verbose:
-                print("Delay offset parameters:")
+            log_str = "Delay offset parameters:"
             for k, v in offsets.items():
                 if k == "creation_date":
                     continue
                 if k == "constant":
                     constant = v
-                    if verbose:
-                        print(f"   Constant: {constant} ")
+                    log_str += f"\n   Constant: {constant}"
                 elif k == "flip_delay_axis":
                     fda = str(v)
                     if fda.lower() in ["true", "1"]:
@@ -330,25 +355,28 @@ def add_offsets(
                         raise ValueError(
                             f"Invalid value for flip_delay_axis in config: {flip_delay_axis}.",
                         )
-                    if verbose:
-                        print(f"   Flip delay axis: {flip_delay_axis} ")
-                else:
-                    columns.append(k)
-                    try:
-                        weight = v["weight"]
-                    except KeyError:
-                        weight = 1
-                    weights.append(weight)
-                    pm = v.get("preserve_mean", False)
-                    preserve_mean.append(pm)
-                    red = v.get("reduction", None)
-                    reductions.append(red)
-                    if verbose:
-                        print(
-                            f"   Column[{k}]: Weight={weight}, Preserve Mean: {pm}, ",
-                            f"Reductions: {red}.",
+                    log_str += f"\n   Flip delay axis: {flip_delay_axis}"
+                elif k == "columns":
+                    for column_name, column_dict in offsets["columns"].items():
+                        columns.append(column_name)
+                        weight = column_dict.get("weight", 1)
+                        if not isinstance(weight, (int, float, np.integer, np.floating)):
+                            raise TypeError(
+                                f"Invalid type for weight of column {column_name}: {type(weight)}",
+                            )
+                        weights.append(weight)
+                        pm = column_dict.get("preserve_mean", False)
+                        preserve_mean.append(pm)
+                        red = column_dict.get("reduction", None)
+                        reductions.append(red)
+                        log_str += (
+                            f"\n   Column[{column_name}]: Weight={weight}, Preserve Mean: {pm}, "
+                            f"Reductions: {red}."
                         )
 
+            if not suppress_output:
+                logger.info(log_str)
+
             if len(columns) > 0:
                 df = dfops.offset_by_other_columns(
                     df=df,
@@ -379,7 +407,7 @@ def extract_delay_stage_parameters(
     p1_key: str,
     p2_key: str,
     t0_key: str,
-) -> Tuple:
+) -> tuple:
     """
     Read delay stage ranges from hdf5 file
 
@@ -404,18 +432,18 @@ def extract_delay_stage_parameters(
 
 
 def mm_to_ps(
-    delay_mm: Union[float, np.ndarray],
+    delay_mm: float | np.ndarray,
     time0_mm: float,
-) -> Union[float, np.ndarray]:
+) -> float | np.ndarray:
     """Converts a delay stage position in mm into a relative delay in picoseconds
     (double pass).
 
     Args:
-        delay_mm (Union[float, Sequence[float]]): Delay stage position in mm
+        delay_mm (float | np.ndarray): Delay stage position in mm
         time0_mm (float): Delay stage position of pump-probe overlap in mm
 
     Returns:
-        Union[float, Sequence[float]]: Relative delay in picoseconds
+        float | np.ndarray: Relative delay in picoseconds
     """
     delay_ps = (delay_mm - time0_mm) / 0.15
     return delay_ps
diff --git a/sed/calibrator/energy.py b/src/sed/calibrator/energy.py
similarity index 81%
rename from sed/calibrator/energy.py
rename to src/sed/calibrator/energy.py
index 98d7cf48..b5d055dd 100644
--- a/sed/calibrator/energy.py
+++ b/src/sed/calibrator/energy.py
@@ -1,19 +1,16 @@
 """sed.calibrator.energy module. Code for energy calibration and
 correction. Mostly ported from https://github.com/mpes-kit/mpes.
 """
+from __future__ import annotations
+
 import itertools as it
-import warnings as wn
+from collections.abc import Sequence
 from copy import deepcopy
 from datetime import datetime
 from functools import partial
 from typing import Any
 from typing import cast
-from typing import Dict
-from typing import List
 from typing import Literal
-from typing import Sequence
-from typing import Tuple
-from typing import Union
 
 import bokeh.plotting as pbk
 import dask.dataframe
@@ -31,15 +28,20 @@
 from IPython.display import display
 from lmfit import Minimizer
 from lmfit import Parameters
-from lmfit.printfuncs import report_fit
+from lmfit.printfuncs import fit_report
 from numpy.linalg import lstsq
 from scipy.signal import savgol_filter
 from scipy.sparse.linalg import lsqr
 
 from sed.binning import bin_dataframe
 from sed.core import dfops
+from sed.core.logging import set_verbosity
+from sed.core.logging import setup_logging
 from sed.loader.base.loader import BaseLoader
 
+# Configure logging
+logger = setup_logging("delay")
+
 
 class EnergyCalibrator:
     """Electron binding energy calibration workflow.
@@ -56,6 +58,8 @@ class EnergyCalibrator:
         tof (np.ndarray, optional): TOF-values for the data traces.
             Defaults to None.
         config (dict, optional): Config dictionary. Defaults to None.
+        verbose (bool, optional): Option to print out diagnostic information.
+            Defaults to True.
     """
 
     def __init__(
@@ -65,6 +69,7 @@ def __init__(
         traces: np.ndarray = None,
         tof: np.ndarray = None,
         config: dict = None,
+        verbose: bool = True,
     ):
         """For the initialization of the EnergyCalibrator class an instance of a
         loader is required. The data can be loaded using the optional arguments,
@@ -78,7 +83,17 @@ def __init__(
             tof (np.ndarray, optional): TOF-values for the data traces.
                 Defaults to None.
             config (dict, optional): Config dictionary. Defaults to None.
+            verbose (bool, optional): Option to print out diagnostic information.
+                Defaults to True.
         """
+        if config is None:
+            config = {}
+
+        self._config = config
+
+        self._verbose = verbose
+        set_verbosity(logger, self._verbose)
+
         self.loader = loader
         self.biases: np.ndarray = None
         self.traces: np.ndarray = None
@@ -88,34 +103,46 @@ def __init__(
         if traces is not None and tof is not None and biases is not None:
             self.load_data(biases=biases, traces=traces, tof=tof)
 
-        if config is None:
-            config = {}
-
-        self._config = config
-
-        self.featranges: List[Tuple] = []  # Value ranges for feature detection
+        self.featranges: list[tuple] = []  # Value ranges for feature detection
         self.peaks: np.ndarray = np.asarray([])
-        self.calibration: Dict[str, Any] = self._config["energy"].get("calibration", {})
-
-        self.tof_column = self._config["dataframe"]["tof_column"]
-        self.tof_ns_column = self._config["dataframe"].get("tof_ns_column", None)
-        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.calibration: dict[str, Any] = self._config["energy"].get("calibration", {})
+
+        self.tof_column = self._config["dataframe"]["columns"]["tof"]
+        self.tof_ns_column = self._config["dataframe"]["columns"].get("tof_ns", None)
+        self.corrected_tof_column = self._config["dataframe"]["columns"]["corrected_tof"]
+        self.energy_column = self._config["dataframe"]["columns"]["energy"]
+        self.x_column = self._config["dataframe"]["columns"]["x"]
+        self.y_column = self._config["dataframe"]["columns"]["y"]
         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["energy"]["tof_width"],
-        ) / 2 ** (self.binning - 1)
-        self.tof_fermi = self._config["energy"]["tof_fermi"] / 2 ** (self.binning - 1)
+        self.tof_width = np.asarray(self._config["energy"]["tof_width"]) / self.binning
+        self.tof_fermi = self._config["energy"]["tof_fermi"] / self.binning
         self.color_clip = self._config["energy"]["color_clip"]
         self.sector_delays = self._config["dataframe"].get("sector_delays", None)
-        self.sector_id_column = self._config["dataframe"].get("sector_id_column", None)
-        self.offsets: Dict[str, Any] = self._config["energy"].get("offsets", {})
-        self.correction: Dict[str, Any] = self._config["energy"].get("correction", {})
+        self.sector_id_column = self._config["dataframe"]["columns"].get("sector_id", None)
+        self.offsets: dict[str, Any] = self._config["energy"].get("offsets", {})
+        self.correction: dict[str, Any] = self._config["energy"].get("correction", {})
+
+    @property
+    def verbose(self) -> bool:
+        """Accessor to the verbosity flag.
+
+        Returns:
+            bool: Verbosity flag.
+        """
+        return self._verbose
+
+    @verbose.setter
+    def verbose(self, verbose: bool):
+        """Setter for the verbosity.
+
+        Args:
+            verbose (bool): Option to turn on verbose output. Sets loglevel to INFO.
+        """
+        self._verbose = verbose
+        set_verbosity(logger, self._verbose)
 
     @property
     def ntraces(self) -> int:
@@ -177,10 +204,10 @@ def load_data(
 
     def bin_data(
         self,
-        data_files: List[str],
-        axes: List[str] = None,
-        bins: List[int] = None,
-        ranges: Sequence[Tuple[float, float]] = None,
+        data_files: list[str],
+        axes: list[str] = None,
+        bins: list[int] = None,
+        ranges: Sequence[tuple[float, float]] = None,
         biases: np.ndarray = None,
         bias_key: str = None,
         **kwds,
@@ -188,12 +215,12 @@ def bin_data(
         """Bin data from single-event files, and load into class.
 
         Args:
-            data_files (List[str]): list of file names to bin
-            axes (List[str], optional): bin axes. Defaults to
-                config["dataframe"]["tof_column"].
-            bins (List[int], optional): number of bins.
+            data_files (list[str]): list of file names to bin
+            axes (list[str], optional): bin axes. Defaults to
+                config["dataframe"]["columns"]["tof"].
+            bins (list[int], optional): number of bins.
                 Defaults to config["energy"]["bins"].
-            ranges (Sequence[Tuple[float, float]], optional): bin ranges.
+            ranges (Sequence[tuple[float, float]], optional): bin ranges.
                 Defaults to config["energy"]["ranges"].
             biases (np.ndarray, optional): Bias voltages used.
                 If not provided, biases are extracted from the file meta data.
@@ -206,26 +233,21 @@ def bin_data(
         if bins is None:
             bins = [self._config["energy"]["bins"]]
         if ranges is None:
-            ranges_ = [
-                np.array(self._config["energy"]["ranges"]) / 2 ** (self.binning - 1),
-            ]
-            ranges = [cast(Tuple[float, float], tuple(v)) for v in ranges_]
+            ranges_ = [np.array(self._config["energy"]["ranges"]) / self.binning]
+            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"])
         try:
-            num_cores = kwds.pop("num_cores", self._config["binning"]["num_cores"])
+            num_cores = kwds.pop("num_cores", self._config["core"]["num_cores"])
         except KeyError:
             num_cores = psutil.cpu_count() - 1
         threads_per_worker = kwds.pop(
             "threads_per_worker",
             self._config["binning"]["threads_per_worker"],
         )
-        threadpool_api = kwds.pop(
-            "threadpool_API",
-            self._config["binning"]["threadpool_API"],
-        )
+        threadpool_api = kwds.pop("threadpool_API", self._config["binning"]["threadpool_API"])
 
         read_biases = False
         if biases is None:
@@ -286,10 +308,11 @@ def normalize(self, smooth: bool = False, span: int = 7, order: int = 1):
             span=span,
             order=order,
         )
+        logger.debug("Normalized energy calibration traces.")
 
     def adjust_ranges(
         self,
-        ranges: Tuple,
+        ranges: tuple,
         ref_id: int = 0,
         traces: np.ndarray = None,
         peak_window: int = 7,
@@ -300,7 +323,7 @@ def adjust_ranges(
         (containing the peaks) among all traces.
 
         Args:
-            ranges (Tuple):
+            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.
@@ -310,8 +333,10 @@ def adjust_ranges(
                 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()``).
+            **kwds: keyword arguments
+                - *labels*: List of labels for plotting. Default uses the bias voltages.
+                - *figsize* Figure size.
+                Additional keyword arguments are passed to ``find_correspondence()``.
         """
         if traces is None:
             traces = self.traces_normed
@@ -320,40 +345,26 @@ def adjust_ranges(
             ranges=ranges,
             ref_id=ref_id,
             traces=traces,
-            infer_others=True,
-            mode="replace",
+            **kwds,
         )
         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))
+        figsize = kwds.pop("figsize", (6, 4))
         plot_segs = []
         plot_peaks = []
         fig, ax = plt.subplots(figsize=figsize)
-        colors = plt.get_cmap("rainbow")(np.linspace(0, 1, len(traces)))
+        colors = it.cycle(plt.rcParams["axes.prop_cycle"].by_key()["color"])
         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],
-            )
+            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,
-            )
+            (line,) = ax.plot(tofseg, traceseg, ls="-", color=color, linewidth=3)
             plot_segs.append(line)
             # markers
             (scatt,) = ax.plot(
@@ -367,9 +378,12 @@ def adjust_ranges(
             plot_peaks.append(scatt)
         ax.legend(fontsize=8, loc="upper right")
         ax.set_title("")
+        plt.xlabel("Time-of-flight")
+        plt.ylabel("Intensity")
+        plt.tight_layout()
 
         def update(refid, ranges):
-            self.add_ranges(ranges, refid, traces=traces)
+            self.add_ranges(ranges, refid, traces=traces, **kwds)
             self.feature_extract(peak_window=7)
             for itr, _ in enumerate(self.traces_normed):
                 seg = self.featranges[itr]
@@ -381,8 +395,8 @@ def update(refid, ranges):
                 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])
+                plot_peaks[itr].set_xdata([self.peaks[itr, 0]])
+                plot_peaks[itr].set_ydata([self.peaks[itr, 1]])
 
             fig.canvas.draw_idle()
 
@@ -413,8 +427,11 @@ def apply_func(apply: bool):  # noqa: ARG001
                 ranges_slider.value,
                 refid_slider.value,
                 traces=self.traces_normed,
+                **kwds,
             )
+            logger.info(f"Use feature ranges: {self.featranges}.")
             self.feature_extract(peak_window=7)
+            logger.info(f"Extracted energy features: {self.peaks}.")
             ranges_slider.close()
             refid_slider.close()
             apply_button.close()
@@ -429,7 +446,7 @@ def apply_func(apply: bool):  # noqa: ARG001
 
     def add_ranges(
         self,
-        ranges: Union[List[Tuple], Tuple],
+        ranges: list[tuple] | tuple,
         ref_id: int = 0,
         traces: np.ndarray = None,
         infer_others: bool = True,
@@ -439,7 +456,7 @@ def add_ranges(
         """Select or extract the equivalent feature ranges (containing the peaks) among all traces.
 
         Args:
-            ranges (Union[List[Tuple], Tuple]):
+            ranges (list[tuple] | 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.
@@ -459,7 +476,7 @@ def add_ranges(
         # Infer the corresponding feature detection range of other traces by alignment
         if infer_others:
             assert isinstance(ranges, tuple)
-            newranges: List[Tuple] = []
+            newranges: list[tuple] = []
 
             for i in range(self.ntraces):
                 pathcorr = find_correspondence(
@@ -482,14 +499,14 @@ def add_ranges(
 
     def feature_extract(
         self,
-        ranges: List[Tuple] = None,
+        ranges: list[tuple] = None,
         traces: np.ndarray = None,
         peak_window: int = 7,
     ):
         """Select or extract the equivalent landmarks (e.g. peaks) among all traces.
 
         Args:
-            ranges (List[Tuple], optional):  List of ranges in each trace to look for
+            ranges (list[tuple], optional):  List of ranges in each trace to look for
                 the peak feature, [start, end]. Defaults to self.featranges.
             traces (np.ndarray, optional): Collection of 1D spectra to use for
                 calibration. Defaults to self.traces_normed.
@@ -514,21 +531,19 @@ def feature_extract(
 
     def calibrate(
         self,
-        ref_id: int = 0,
+        ref_energy: float = 0,
         method: str = "lmfit",
         energy_scale: str = "kinetic",
         landmarks: np.ndarray = None,
         biases: np.ndarray = None,
         t: np.ndarray = None,
-        verbose: bool = True,
         **kwds,
     ) -> dict:
         """Calculate the functional mapping between time-of-flight and the energy
         scale using optimization methods.
 
         Args:
-            ref_id (int, optional): The reference trace index (an integer).
-                Defaults to 0.
+            ref_energy (float): Binding/kinetic energy of the detected feature.
             method (str, optional):  Method for determining the energy calibration.
 
                 - **'lmfit'**: Energy calibration using lmfit and 1/t^2 form.
@@ -545,8 +560,6 @@ def calibrate(
                 calibration. Defaults to self.peaks.
             biases (np.ndarray, optional): Bias values. Defaults to self.biases.
             t (np.ndarray, optional): TOF values. Defaults to self.tof.
-            verbose (bool, optional): Option to print out diagnostic information.
-                Defaults to True.
             **kwds: keyword arguments.
                 See available keywords for ``poly_energy_calibration()`` and
                 ``fit_energy_calibration()``
@@ -583,17 +596,16 @@ def calibrate(
                 sign * biases,
                 binwidth,
                 binning,
-                ref_id=ref_id,
+                ref_energy=ref_energy,
                 t=t,
                 energy_scale=energy_scale,
-                verbose=verbose,
                 **kwds,
             )
         elif method in ("lstsq", "lsqr"):
             self.calibration = poly_energy_calibration(
                 landmarks,
                 sign * biases,
-                ref_id=ref_id,
+                ref_energy=ref_energy,
                 aug=self.dup,
                 method=method,
                 t=t,
@@ -603,13 +615,13 @@ def calibrate(
         else:
             raise NotImplementedError()
 
-        self.calibration["creation_date"] = datetime.now().timestamp()
+        self.calibration["creation_date"] = datetime.now()
         return self.calibration
 
-    def view(  # pylint: disable=dangerous-default-value
+    def view(
         self,
         traces: np.ndarray,
-        segs: List[Tuple] = None,
+        segs: list[tuple] = None,
         peaks: np.ndarray = None,
         show_legend: bool = True,
         backend: str = "matplotlib",
@@ -623,7 +635,7 @@ def view(  # pylint: disable=dangerous-default-value
 
         Args:
             traces (np.ndarray): Matrix of traces to visualize.
-            segs (List[Tuple], optional): Segments to be highlighted in the
+            segs (list[tuple], optional): Segments to be highlighted in the
                 visualization. Defaults to None.
             peaks (np.ndarray, optional): Peak positions for labelling the traces.
                 Defaults to None.
@@ -655,15 +667,19 @@ def view(  # pylint: disable=dangerous-default-value
 
         sign = 1 if energy_scale == "kinetic" else -1
 
+        figsize = kwds.pop("figsize", (6, 4))
+
         if backend == "matplotlib":
-            figsize = kwds.pop("figsize", (12, 4))
-            fig, ax = plt.subplots(figsize=figsize)
-            for itr, trace in enumerate(traces):
+            colors = it.cycle(plt.rcParams["axes.prop_cycle"].by_key()["color"])
+            _, ax = plt.subplots(figsize=figsize)
+            for itr, color in zip(range(len(traces)), colors):
+                trace = traces[itr, :]
                 if align:
                     ax.plot(
-                        xaxis + sign * (self.biases[itr] - self.biases[self.calibration["refid"]]),
+                        xaxis + sign * (self.biases[itr]),
                         trace,
                         ls="-",
+                        color=color,
                         linewidth=1,
                         label=lbs[itr],
                         **linekwds,
@@ -673,6 +689,7 @@ def view(  # pylint: disable=dangerous-default-value
                         xaxis,
                         trace,
                         ls="-",
+                        color=color,
                         linewidth=1,
                         label=lbs[itr],
                         **linekwds,
@@ -687,6 +704,7 @@ def view(  # pylint: disable=dangerous-default-value
                         tofseg,
                         traceseg,
                         ls="-",
+                        color=color,
                         linewidth=2,
                         **linesegkwds,
                     )
@@ -701,7 +719,7 @@ def view(  # pylint: disable=dangerous-default-value
 
             if show_legend:
                 try:
-                    ax.legend(fontsize=12, **legkwds)
+                    ax.legend(fontsize=8, loc="upper right", **legkwds)
                 except TypeError:
                     pass
 
@@ -712,11 +730,10 @@ def view(  # pylint: disable=dangerous-default-value
             colors = it.cycle(ColorCycle[10])
             ttp = [("(x, y)", "($x, $y)")]
 
-            figsize = kwds.pop("figsize", (800, 300))
             fig = pbk.figure(
                 title=ttl,
-                width=figsize[0],
-                height=figsize[1],
+                width=figsize[0] * 100,
+                height=figsize[1] * 100,
                 tooltips=ttp,
             )
             # Plotting the main traces
@@ -724,7 +741,7 @@ def view(  # pylint: disable=dangerous-default-value
                 trace = traces[itr, :]
                 if align:
                     fig.line(
-                        xaxis + sign * (self.biases[itr] - self.biases[self.calibration["refid"]]),
+                        xaxis + sign * (self.biases[itr]),
                         trace,
                         color=color,
                         line_dash="solid",
@@ -779,27 +796,31 @@ def view(  # pylint: disable=dangerous-default-value
 
     def append_energy_axis(
         self,
-        df: Union[pd.DataFrame, dask.dataframe.DataFrame],
+        df: pd.DataFrame | dask.dataframe.DataFrame,
         tof_column: str = None,
         energy_column: str = None,
         calibration: dict = None,
-        verbose: bool = True,
+        bias_voltage: float = None,
+        suppress_output: bool = False,
         **kwds,
-    ) -> Tuple[Union[pd.DataFrame, dask.dataframe.DataFrame], dict]:
+    ) -> tuple[pd.DataFrame | dask.dataframe.DataFrame, dict]:
         """Calculate and append the energy axis to the events dataframe.
 
         Args:
-            df (Union[pd.DataFrame, dask.dataframe.DataFrame]):
+            df (pd.DataFrame | dask.dataframe.DataFrame):
                 Dataframe to apply the energy axis calibration to.
             tof_column (str, optional): Label of the source column.
-                Defaults to config["dataframe"]["tof_column"].
+                Defaults to config["dataframe"]["columns"]["tof"].
             energy_column (str, optional): Label of the destination column.
-                Defaults to config["dataframe"]["energy_column"].
+                Defaults to config["dataframe"]["columns"]["energy"].
             calibration (dict, optional): Calibration dictionary. If provided,
                 overrides calibration from class or config.
                 Defaults to self.calibration or config["energy"]["calibration"].
-            verbose (bool, optional): Option to print out diagnostic information.
-                Defaults to True.
+            bias_voltage (float, optional): Sample bias voltage of the scan data. If omitted,
+                the bias voltage is being read from the dataframe. If it is not found there,
+                a warning is printed and the calibrated data might have an offset.
+            verbose (bool, optional): Option to suppress output of diagnostic information.
+                Defaults to False.
             **kwds: additional keyword arguments for the energy conversion. They are
                 added to the calibration dictionary.
 
@@ -808,7 +829,7 @@ def append_energy_axis(
             NotImplementedError: Raised if an invalid calib_type is found.
 
         Returns:
-            Union[pd.DataFrame, dask.dataframe.DataFrame]: dataframe with added column
+            tuple[pd.DataFrame | dask.dataframe.DataFrame, dict]: dataframe with added column
             and energy calibration metadata dictionary.
         """
         if tof_column is None:
@@ -830,13 +851,11 @@ def append_energy_axis(
         if len(kwds) > 0:
             for key, value in kwds.items():
                 calibration[key] = value
-            calibration["creation_date"] = datetime.now().timestamp()
+            calibration["creation_date"] = datetime.now()
 
-        elif "creation_date" in calibration and verbose:
-            datestring = datetime.fromtimestamp(calibration["creation_date"]).strftime(
-                "%m/%d/%Y, %H:%M:%S",
-            )
-            print(f"Using energy calibration parameters generated on {datestring}")
+        elif "creation_date" in calibration and not suppress_output:
+            datestring = calibration["creation_date"].strftime("%m/%d/%Y, %H:%M:%S")
+            logger.info(f"Using energy calibration parameters generated on {datestring}")
 
         # try to determine calibration type if not provided
         if "calib_type" not in calibration:
@@ -847,6 +866,8 @@ def append_energy_axis(
 
             elif "coeffs" in calibration and "E0" in calibration:
                 calibration["calib_type"] = "poly"
+                if "energy_scale" not in calibration:
+                    calibration["energy_scale"] = "kinetic"
             else:
                 raise ValueError("No valid calibration parameters provided!")
 
@@ -886,36 +907,75 @@ def append_energy_axis(
         else:
             raise NotImplementedError
 
+        if not suppress_output:
+            report_dict = {
+                "calib_type": calibration["calib_type"],
+                "fit_function": calibration["fit_function"],
+                "coefficients": calibration["coefficients"],
+            }
+            logger.debug(f"Used energy calibration parameters: {report_dict}.")
+
+        # apply bias offset
+        scale_sign: Literal[-1, 1] = -1 if calibration["energy_scale"] == "binding" else 1
+        if bias_voltage is not None:
+            df[energy_column] = df[energy_column] + scale_sign * bias_voltage
+            if not suppress_output:
+                logger.debug(f"Shifted energy column by constant bias value: {bias_voltage}.")
+        elif self._config["dataframe"]["columns"]["bias"] in df.columns:
+            df = dfops.offset_by_other_columns(
+                df=df,
+                target_column=energy_column,
+                offset_columns=self._config["dataframe"]["columns"]["bias"],
+                weights=scale_sign,
+            )
+            if not suppress_output:
+                logger.debug(
+                    "Shifted energy column by bias column: "
+                    f"{self._config['dataframe']['columns']['bias']}.",
+                )
+        else:
+            logger.warning(
+                "Sample bias data not found or provided. Calibrated energy might be incorrect.",
+            )
+
         metadata = self.gather_calibration_metadata(calibration)
 
         return df, metadata
 
     def append_tof_ns_axis(
         self,
-        df: Union[pd.DataFrame, dask.dataframe.DataFrame],
+        df: pd.DataFrame | dask.dataframe.DataFrame,
         tof_column: str = None,
         tof_ns_column: str = None,
         **kwds,
-    ) -> Tuple[Union[pd.DataFrame, dask.dataframe.DataFrame], dict]:
+    ) -> tuple[pd.DataFrame | dask.dataframe.DataFrame, dict]:
         """Converts the time-of-flight time from steps to time in ns.
 
         Args:
-            df (Union[pd.DataFrame, dask.dataframe.DataFrame]): Dataframe to convert.
+            df (pd.DataFrame | dask.dataframe.DataFrame): Dataframe to convert.
             tof_column (str, optional): Name of the column containing the
-                time-of-flight steps. Defaults to config["dataframe"]["tof_column"].
+                time-of-flight steps. Defaults to config["dataframe"]["columns"]["tof"].
             tof_ns_column (str, optional): Name of the column to store the
-                time-of-flight in nanoseconds. Defaults to config["dataframe"]["tof_ns_column"].
+                time-of-flight in nanoseconds. Defaults to config["dataframe"]["columns"]["tof_ns"].
             binwidth (float, optional): Time-of-flight binwidth in ns.
                 Defaults to config["energy"]["tof_binwidth"].
             binning (int, optional): Time-of-flight binning factor.
                 Defaults to config["energy"]["tof_binning"].
+            **kwds:
+
+                - **binwidth**: Binwidth in ns.
+                - **binning**: Binning factor of the TOF column.
 
         Returns:
-            dask.dataframe.DataFrame: Dataframe with the new columns.
-            dict: Metadata dictionary.
+            tuple[pd.DataFrame | dask.dataframe.DataFrame, dict]: Dataframe with the new columns
+            and Metadata dictionary.
         """
         binwidth = kwds.pop("binwidth", self.binwidth)
         binning = kwds.pop("binning", self.binning)
+
+        if len(kwds) > 0:
+            raise TypeError(f"append_tof_ns_axis() got unexpected keyword arguments {kwds.keys()}.")
+
         if tof_column is None:
             if self.corrected_tof_column in df.columns:
                 tof_column = self.corrected_tof_column
@@ -930,7 +990,7 @@ def append_tof_ns_axis(
             binning,
             df[tof_column].astype("float64"),
         )
-        metadata: Dict[str, Any] = {
+        metadata: dict[str, Any] = {
             "applied": True,
             "binwidth": binwidth,
             "binning": binning,
@@ -949,7 +1009,7 @@ def gather_calibration_metadata(self, calibration: dict = None) -> dict:
         """
         if calibration is None:
             calibration = self.calibration
-        metadata: Dict[Any, Any] = {}
+        metadata: dict[Any, Any] = {}
         metadata["applied"] = True
         metadata["calibration"] = deepcopy(calibration)
         metadata["tof"] = deepcopy(self.tof)
@@ -966,7 +1026,7 @@ def adjust_energy_correction(
         image: xr.DataArray,
         correction_type: str = None,
         amplitude: float = None,
-        center: Tuple[float, float] = None,
+        center: tuple[float, float] = None,
         correction: dict = None,
         apply: bool = False,
         **kwds,
@@ -986,7 +1046,7 @@ def adjust_energy_correction(
                 Defaults to config["energy"]["correction_type"].
             amplitude (float, optional): Amplitude of the time-of-flight correction
                 term. Defaults to config["energy"]["correction"]["correction_type"].
-            center (Tuple[float, float], optional): Center (x/y) coordinates for the
+            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.
@@ -1137,9 +1197,9 @@ def update(amplitude, x_center, y_center, **kwds):
             )
 
             trace1.set_ydata(correction_x)
-            line1.set_xdata(x=x_center)
+            line1.set_xdata([x_center])
             trace2.set_ydata(correction_y)
-            line2.set_xdata(x=y_center)
+            line2.set_xdata([y_center])
 
             fig.canvas.draw_idle()
 
@@ -1148,7 +1208,7 @@ def common_apply_func(apply: bool):  # noqa: ARG001
             self.correction["amplitude"] = correction["amplitude"]
             self.correction["center"] = correction["center"]
             self.correction["correction_type"] = correction["correction_type"]
-            self.correction["creation_date"] = datetime.now().timestamp()
+            self.correction["creation_date"] = datetime.now()
             amplitude_slider.close()
             x_center_slider.close()
             y_center_slider.close()
@@ -1312,24 +1372,24 @@ def apply_func(apply: bool):
 
     def apply_energy_correction(
         self,
-        df: Union[pd.DataFrame, dask.dataframe.DataFrame],
+        df: pd.DataFrame | dask.dataframe.DataFrame,
         tof_column: str = None,
         new_tof_column: str = None,
         correction_type: str = None,
         amplitude: float = None,
         correction: dict = None,
-        verbose: bool = True,
+        suppress_output: bool = False,
         **kwds,
-    ) -> Tuple[Union[pd.DataFrame, dask.dataframe.DataFrame], dict]:
+    ) -> tuple[pd.DataFrame | dask.dataframe.DataFrame, dict]:
         """Apply correction to the time-of-flight (TOF) axis of single-event data.
 
         Args:
-            df (Union[pd.DataFrame, dask.dataframe.DataFrame]): The dataframe where
+            df (pd.DataFrame | dask.dataframe.DataFrame): The dataframe where
                 to apply the energy correction to.
             tof_column (str, optional): Name of the source column to convert.
-                Defaults to config["dataframe"]["tof_column"].
+                Defaults to config["dataframe"]["columns"]["tof"].
             new_tof_column (str, optional): Name of the destination column to convert.
-                Defaults to config["dataframe"]["corrected_tof_column"].
+                Defaults to config["dataframe"]["columns"]["corrected_tof"].
             correction_type (str, optional): Type of correction to apply to the TOF
                 axis. Valid values are:
 
@@ -1344,8 +1404,8 @@ def apply_energy_correction(
             correction (dict, optional): Correction dictionary containing parameters
                 for the correction. Defaults to self.correction or
                 config["energy"]["correction"].
-            verbose (bool, optional): Option to print out diagnostic information.
-                Defaults to True.
+            suppress_output (bool, optional): Option to suppress output of diagnostic information.
+                Defaults to False.
             **kwds: Additional parameters to use for the correction:
 
                 - **x_column** (str): Name of the x column.
@@ -1361,7 +1421,7 @@ def apply_energy_correction(
                   asymmetric 2D Lorentz profile, X-direction.
 
         Returns:
-            Union[pd.DataFrame, dask.dataframe.DataFrame]: dataframe with added column
+            tuple[pd.DataFrame | dask.dataframe.DataFrame, dict]: dataframe with added column
             and Energy correction metadata dictionary.
         """
         if correction is None:
@@ -1386,18 +1446,19 @@ def apply_energy_correction(
             for key, value in kwds.items():
                 correction[key] = value
 
-            correction["creation_date"] = datetime.now().timestamp()
+            correction["creation_date"] = datetime.now()
 
-        elif "creation_date" in correction and verbose:
-            datestring = datetime.fromtimestamp(correction["creation_date"]).strftime(
-                "%m/%d/%Y, %H:%M:%S",
-            )
-            print(f"Using energy correction parameters generated on {datestring}")
+        elif "creation_date" in correction and not suppress_output:
+            datestring = correction["creation_date"].strftime("%m/%d/%Y, %H:%M:%S")
+            logger.info(f"Using energy correction parameters generated on {datestring}")
 
         missing_keys = {"correction_type", "center", "amplitude"} - set(correction.keys())
         if missing_keys:
             raise ValueError(f"Required correction parameters '{missing_keys}' missing!")
 
+        if not suppress_output:
+            logger.debug(f"Correction parameters:\n{correction}")
+
         df[new_tof_column] = df[tof_column] + correction_function(
             x=df[x_column],
             y=df[y_column],
@@ -1419,7 +1480,7 @@ def gather_correction_metadata(self, correction: dict = None) -> dict:
         """
         if correction is None:
             correction = self.correction
-        metadata: Dict[Any, Any] = {}
+        metadata: dict[Any, Any] = {}
         metadata["applied"] = True
         metadata["correction"] = deepcopy(correction)
 
@@ -1431,21 +1492,21 @@ def align_dld_sectors(
         tof_column: str = None,
         sector_id_column: str = None,
         sector_delays: np.ndarray = None,
-    ) -> Tuple[dask.dataframe.DataFrame, dict]:
+    ) -> tuple[dask.dataframe.DataFrame, dict]:
         """Aligns the time-of-flight axis of the different sections of a detector.
 
         Args:
-            df (Union[pd.DataFrame, dask.dataframe.DataFrame]): Dataframe to use.
+            df (dask.dataframe.DataFrame): Dataframe to use.
             tof_column (str, optional): Name of the column containing the time-of-flight values.
-                Defaults to config["dataframe"]["tof_column"].
+                Defaults to config["dataframe"]["columns"]["tof"].
             sector_id_column (str, optional): Name of the column containing the sector id values.
-                Defaults to config["dataframe"]["sector_id_column"].
+                Defaults to config["dataframe"]["columns"]["sector_id"].
             sector_delays (np.ndarray, optional): Array containing the sector delays. Defaults to
                 config["dataframe"]["sector_delays"].
 
         Returns:
-            dask.dataframe.DataFrame: Dataframe with the new columns.
-            dict: Metadata dictionary.
+            tuple[dask.dataframe.DataFrame, dict]: Dataframe with the new columns and Metadata
+            dictionary.
         """
         if sector_delays is None:
             sector_delays = self.sector_delays
@@ -1467,7 +1528,7 @@ def align_sector(x):
             return val.astype(np.float32)
 
         df[tof_column] = df.map_partitions(align_sector, meta=(tof_column, np.float32))
-        metadata: Dict[str, Any] = {
+        metadata: dict[str, Any] = {
             "applied": True,
             "sector_delays": sector_delays,
         }
@@ -1475,16 +1536,16 @@ def align_sector(x):
 
     def add_offsets(
         self,
-        df: Union[pd.DataFrame, dask.dataframe.DataFrame] = None,
-        offsets: Dict[str, Any] = None,
+        df: pd.DataFrame | dask.dataframe.DataFrame = None,
+        offsets: dict[str, Any] = None,
         constant: float = None,
-        columns: Union[str, Sequence[str]] = None,
-        weights: Union[float, Sequence[float]] = None,
-        preserve_mean: Union[bool, Sequence[bool]] = False,
-        reductions: Union[str, Sequence[str]] = None,
+        columns: str | Sequence[str] = None,
+        weights: float | Sequence[float] = None,
+        preserve_mean: bool | Sequence[bool] = False,
+        reductions: str | Sequence[str] = None,
         energy_column: str = None,
-        verbose: bool = True,
-    ) -> Tuple[Union[pd.DataFrame, dask.dataframe.DataFrame], dict]:
+        suppress_output: bool = False,
+    ) -> tuple[pd.DataFrame | dask.dataframe.DataFrame, dict]:
         """Apply an offset to the energy column by the values of the provided columns.
 
         If no parameter is passed to this function, the offset is applied as defined in the
@@ -1492,25 +1553,26 @@ def add_offsets(
         and the offset is applied using the ``dfops.apply_offset_from_columns()`` function.
 
         Args:
-            df (Union[pd.DataFrame, dask.dataframe.DataFrame]): Dataframe to use.
+            df (pd.DataFrame | dask.dataframe.DataFrame): Dataframe to use.
             offsets (Dict, optional): Dictionary of energy offset parameters.
             constant (float, optional): The constant to shift the energy axis by.
-            columns (Union[str, Sequence[str]]): Name of the column(s) to apply the shift from.
-            weights (Union[float, Sequence[float]]): weights to apply to the columns.
+            columns (str | Sequence[str]): Name of the column(s) to apply the shift from.
+            weights (float | Sequence[float]): weights to apply to the columns.
                 Can also be used to flip the sign (e.g. -1). Defaults to 1.
-            preserve_mean (bool): Whether to subtract the mean of the column before applying the
-                shift. Defaults to False.
-            reductions (str): The reduction to apply to the column. Should be an available method
-                of dask.dataframe.Series. For example "mean". In this case the function is applied
-                to the column to generate a single value for the whole dataset. If None, the shift
-                is applied per-dataframe-row. Defaults to None. Currently only "mean" is supported.
+            preserve_mean (bool | Sequence[bool]): Whether to subtract the mean of the column
+                before applying the shift. Defaults to False.
+            reductions (str | Sequence[str]): The reduction to apply to the column. Should be an
+                available method of dask.dataframe.Series. For example "mean". In this case the
+                function is applied to the column to generate a single value for the whole dataset.
+                If None, the shift is applied per-dataframe-row. Defaults to None. Currently only
+                "mean" is supported.
             energy_column (str, optional): Name of the column containing the energy values.
-            verbose (bool, optional): Option to print out diagnostic information.
-                Defaults to True.
+            suppress_output (bool, optional): Option to suppress output of diagnostic information.
+                Defaults to False.
 
         Returns:
-            dask.dataframe.DataFrame: Dataframe with the new columns.
-            dict: Metadata dictionary.
+            tuple[pd.DataFrame | dask.dataframe.DataFrame, dict]: Dataframe with the new columns
+            and Metadata dictionary.
         """
         if offsets is None:
             offsets = deepcopy(self.offsets)
@@ -1518,7 +1580,7 @@ def add_offsets(
         if energy_column is None:
             energy_column = self.energy_column
 
-        metadata: Dict[str, Any] = {
+        metadata: dict[str, Any] = {
             "applied": True,
         }
 
@@ -1534,9 +1596,13 @@ def add_offsets(
             # pylint:disable=duplicate-code
             # use passed parameters, overwrite config
             offsets = {}
-            offsets["creation_date"] = datetime.now().timestamp()
+            offsets["creation_date"] = datetime.now()
             # column-based offsets
             if columns is not None:
+                offsets["columns"] = {}
+                if isinstance(columns, str):
+                    columns = [columns]
+
                 if weights is None:
                     weights = 1
                 if isinstance(weights, (int, float, np.integer, np.floating)):
@@ -1548,10 +1614,13 @@ def add_offsets(
                 if not all(isinstance(s, (int, float, np.integer, np.floating)) for s in weights):
                     raise TypeError(f"Invalid type for weights: {type(weights)}")
 
-                if isinstance(columns, str):
-                    columns = [columns]
-                if isinstance(preserve_mean, bool):
-                    preserve_mean = [preserve_mean] * len(columns)
+                if preserve_mean is None:
+                    preserve_mean = False
+                if not isinstance(preserve_mean, Sequence):
+                    preserve_mean = [preserve_mean]
+                if len(preserve_mean) == 1:
+                    preserve_mean = [preserve_mean[0]] * len(columns)
+
                 if not isinstance(reductions, Sequence):
                     reductions = [reductions]
                 if len(reductions) == 1:
@@ -1559,7 +1628,7 @@ def add_offsets(
 
                 # store in offsets dictionary
                 for col, weight, pmean, red in zip(columns, weights, preserve_mean, reductions):
-                    offsets[col] = {
+                    offsets["columns"][col] = {
                         "weight": weight,
                         "preserve_mean": pmean,
                         "reduction": red,
@@ -1571,11 +1640,9 @@ def add_offsets(
             elif constant is not None:
                 raise TypeError(f"Invalid type for constant: {type(constant)}")
 
-        elif "creation_date" in offsets and verbose:
-            datestring = datetime.fromtimestamp(offsets["creation_date"]).strftime(
-                "%m/%d/%Y, %H:%M:%S",
-            )
-            print(f"Using energy offset parameters generated on {datestring}")
+        elif "creation_date" in offsets and not suppress_output:
+            datestring = offsets["creation_date"].strftime("%m/%d/%Y, %H:%M:%S")
+            logger.info(f"Using energy offset parameters generated on {datestring}")
 
         if len(offsets) > 0:
             # unpack dictionary
@@ -1584,43 +1651,39 @@ def add_offsets(
             weights = []
             preserve_mean = []
             reductions = []
-            if verbose:
-                print("Energy offset parameters:")
+            log_str = "Energy offset parameters:"
             for k, v in offsets.items():
                 if k == "creation_date":
                     continue
-                if k == "constant":
+                elif k == "constant":
                     # flip sign if binding energy scale
                     constant = v * scale_sign
-                    if verbose:
-                        print(f"   Constant: {constant} ")
-                else:
-                    columns.append(k)
-                    try:
-                        weight = v["weight"]
-                    except KeyError:
-                        weight = 1
-                    if not isinstance(weight, (int, float, np.integer, np.floating)):
-                        raise TypeError(f"Invalid type for weight of column {k}: {type(weight)}")
-                    # flip sign if binding energy scale
-                    weight = weight * scale_sign
-                    weights.append(weight)
-                    pm = v.get("preserve_mean", False)
-                    if str(pm).lower() in ["false", "0", "no"]:
-                        pm = False
-                    elif str(pm).lower() in ["true", "1", "yes"]:
-                        pm = True
-                    preserve_mean.append(pm)
-                    red = v.get("reduction", None)
-                    if str(red).lower() in ["none", "null"]:
-                        red = None
-                    reductions.append(red)
-                    if verbose:
-                        print(
-                            f"   Column[{k}]: Weight={weight}, Preserve Mean: {pm}, ",
-                            f"Reductions: {red}.",
+                    log_str += f"\n   Constant: {constant}"
+                elif k == "columns":
+                    for column_name, column_dict in offsets["columns"].items():
+                        columns.append(column_name)
+                        weight = column_dict.get("weight", 1)
+                        if not isinstance(weight, (int, float, np.integer, np.floating)):
+                            raise TypeError(
+                                f"Invalid type for weight of column {column_name}: {type(weight)}",
+                            )
+                        # flip sign if binding energy scale
+                        weight = weight * scale_sign
+                        weights.append(weight)
+                        pm = column_dict.get("preserve_mean", False)
+                        preserve_mean.append(pm)
+                        red = column_dict.get("reduction", None)
+                        if str(red).lower() in ["none", "null"]:
+                            red = None
+                        reductions.append(red)
+                        log_str += (
+                            f"\n   Column[{column_name}]: Weight={weight}, Preserve Mean: {pm}, "
+                            f"Reductions: {red}."
                         )
 
+            if not suppress_output:
+                logger.info(log_str)
+
             if len(columns) > 0:
                 df = dfops.offset_by_other_columns(
                     df=df,
@@ -1635,10 +1698,7 @@ def add_offsets(
         if constant:
             if not isinstance(constant, (int, float, np.integer, np.floating)):
                 raise TypeError(f"Invalid type for constant: {type(constant)}")
-            df[energy_column] = df.map_partitions(
-                lambda x: x[energy_column] + constant,
-                meta=(energy_column, np.float64),
-            )
+            df[energy_column] = df[energy_column] + constant
 
         self.offsets = offsets
         metadata["offsets"] = offsets
@@ -1646,17 +1706,17 @@ def add_offsets(
         return df, metadata
 
 
-def extract_bias(files: List[str], bias_key: str) -> np.ndarray:
+def extract_bias(files: list[str], bias_key: str) -> np.ndarray:
     """Read bias values from hdf5 files
 
     Args:
-        files (List[str]): List of filenames
+        files (list[str]): List of filenames
         bias_key (str): hdf5 path to the bias value
 
     Returns:
         np.ndarray: Array of bias values.
     """
-    bias_list: List[float] = []
+    bias_list: list[float] = []
     for file in files:
         with h5py.File(file, "r") as file_handle:
             if bias_key[0] == "@":
@@ -1668,21 +1728,21 @@ def extract_bias(files: List[str], bias_key: str) -> np.ndarray:
 
 
 def correction_function(
-    x: Union[float, np.ndarray],
-    y: Union[float, np.ndarray],
+    x: float | np.ndarray,
+    y: float | np.ndarray,
     correction_type: str,
-    center: Tuple[float, float],
+    center: tuple[float, float],
     amplitude: float,
     **kwds,
-) -> Union[float, np.ndarray]:
+) -> float | np.ndarray:
     """Calculate the TOF correction based on the given X/Y coordinates and a model.
 
     Args:
-        x (float): x coordinate
-        y (float): y coordinate
+        x (float | np.ndarray): x coordinate
+        y (float | np.ndarray): y coordinate
         correction_type (str): type of correction. One of
             "spherical", "Lorentzian", "Gaussian", or "Lorentzian_asymmetric"
-        center (Tuple[int, int]): center position of the distribution (x,y)
+        center (tuple[int, int]): center position of the distribution (x,y)
         amplitude (float): Amplitude of the correction
         **kwds: Keyword arguments:
 
@@ -1697,7 +1757,7 @@ def correction_function(
               asymmetric 2D Lorentz profile, X-direction.
 
     Returns:
-        float: calculated correction value
+        float | np.ndarray: calculated correction value
     """
     if correction_type == "spherical":
         try:
@@ -1835,6 +1895,8 @@ def find_correspondence(
         sig_still (np.ndarray): Reference 1D signals.
         sig_mov (np.ndarray): 1D signal to be aligned.
         **kwds: keyword arguments for ``fastdtw.fastdtw()``
+            - *dist_metric*: Distance metric to use for time warping. Defaults to None.
+            - *radius*: Radius to use for time warping. Defaults to 1.
 
     Returns:
         np.ndarray: Pixel-wise path correspondences between two input 1D arrays
@@ -1842,27 +1904,31 @@ def find_correspondence(
     """
     dist = kwds.pop("dist_metric", None)
     rad = kwds.pop("radius", 1)
+
+    if len(kwds) > 0:
+        raise TypeError(f"find_correspondence() got unexpected keyword arguments {kwds.keys()}.")
+
     _, pathcorr = fastdtw(sig_still, sig_mov, dist=dist, radius=rad)
     return np.asarray(pathcorr)
 
 
 def range_convert(
     x: np.ndarray,
-    xrng: Tuple,
+    xrng: tuple,
     pathcorr: np.ndarray,
-) -> Tuple:
+) -> tuple:
     """Convert value range using a pairwise path correspondence (e.g. obtained
     from time warping algorithm).
 
     Args:
         x (np.ndarray): Values of the x axis (e.g. time-of-flight values).
-        xrng (Tuple): Boundary value range on the x axis.
+        xrng (tuple): Boundary value range on the x axis.
         pathcorr (np.ndarray): Path correspondence between two 1D arrays in the
             following form,
             [(id_1_trace_1, id_1_trace_2), (id_2_trace_1, id_2_trace_2), ...]
 
     Returns:
-        Tuple: Transformed range according to the path correspondence.
+        tuple: Transformed range according to the path correspondence.
     """
     pathcorr = np.asarray(pathcorr)
     xrange_trans = []
@@ -1892,7 +1958,7 @@ def find_nearest(val: float, narray: np.ndarray) -> int:
 def peaksearch(
     traces: np.ndarray,
     tof: np.ndarray,
-    ranges: List[Tuple] = None,
+    ranges: list[tuple] = None,
     pkwindow: int = 3,
     plot: bool = False,
 ) -> np.ndarray:
@@ -1901,7 +1967,7 @@ def peaksearch(
     Args:
         traces (np.ndarray): Collection of 1D spectra.
         tof (np.ndarray): Time-of-flight values.
-        ranges (List[Tuple], optional): List of ranges for peak detection in the format
+        ranges (list[tuple], optional): List of ranges for peak detection in the format
         [(LowerBound1, UpperBound1), (LowerBound2, UpperBound2), ....].
             Defaults to None.
         pkwindow (int, optional): Window width of a peak (amounts to lookahead in
@@ -1924,7 +1990,7 @@ def peaksearch(
         try:
             pkmaxs.append(maxs[0, :])
         except IndexError:  # No peak found for this range
-            print(f"No peak detected in range {rng}.")
+            logger.error(f"No peak detected in range {rng}.")
             raise
 
         if plot:
@@ -1940,7 +2006,7 @@ def peaksearch(
 def _datacheck_peakdetect(
     x_axis: np.ndarray,
     y_axis: np.ndarray,
-) -> Tuple[np.ndarray, np.ndarray]:
+) -> tuple[np.ndarray, np.ndarray]:
     """Input format checking for 1D peakdetect algorithm
 
     Args:
@@ -1951,7 +2017,7 @@ def _datacheck_peakdetect(
         ValueError: Raised if x and y values don't have the same length.
 
     Returns:
-        Tuple[np.ndarray, np.ndarray]: Tuple of checked (x/y) arrays.
+        tuple[np.ndarray, np.ndarray]: Tuple of checked (x/y) arrays.
     """
 
     if x_axis is None:
@@ -1974,7 +2040,7 @@ def peakdetect1d(
     x_axis: np.ndarray = None,
     lookahead: int = 200,
     delta: int = 0,
-) -> Tuple[np.ndarray, np.ndarray]:
+) -> tuple[np.ndarray, np.ndarray]:
     """Function for detecting local maxima and minima in a signal.
     Discovers peaks by searching for values which are surrounded by lower
     or larger values for maxima and minima respectively
@@ -2001,7 +2067,7 @@ def peakdetect1d(
         ValueError: Raised if lookahead and delta are out of range.
 
     Returns:
-        Tuple[np.ndarray, np.ndarray]: Tuple of positions of the positive peaks,
+        tuple[np.ndarray, np.ndarray]: Tuple of positions of the positive peaks,
         positions of the negative peaks
     """
     max_peaks = []
@@ -2022,7 +2088,7 @@ def peakdetect1d(
 
     # maxima and minima candidates are temporarily stored in
     # mx and mn respectively
-    _min, _max = np.Inf, -np.Inf
+    _min, _max = np.inf, -np.inf
 
     # Only detect peak if there is 'lookahead' amount of points after it
     for index, (x, y) in enumerate(
@@ -2037,15 +2103,15 @@ def peakdetect1d(
             _min_pos = x
 
         # Find local maxima
-        if y < _max - delta and _max != np.Inf:
+        if y < _max - delta and _max != np.inf:
             # Maxima peak candidate found
             # look ahead in signal to ensure that this is a peak and not jitter
             if y_axis[index : index + lookahead].max() < _max:
                 max_peaks.append([_max_pos, _max])
                 dump.append(True)
                 # Set algorithm to only find minima now
-                _max = np.Inf
-                _min = np.Inf
+                _max = np.inf
+                _min = np.inf
 
                 if index + lookahead >= length:
                     # The end is within lookahead no more peaks can be found
@@ -2056,15 +2122,15 @@ def peakdetect1d(
             #    mxpos = x_axis[np.where(y_axis[index:index+lookahead]==mx)]
 
         # Find local minima
-        if y > _min + delta and _min != -np.Inf:
+        if y > _min + delta and _min != -np.inf:
             # Minima peak candidate found
             # look ahead in signal to ensure that this is a peak and not jitter
             if y_axis[index : index + lookahead].min() > _min:
                 min_peaks.append([_min_pos, _min])
                 dump.append(False)
                 # Set algorithm to only find maxima now
-                _min = -np.Inf
-                _max = -np.Inf
+                _min = -np.inf
+                _max = -np.inf
 
                 if index + lookahead >= length:
                     # The end is within lookahead no more peaks can be found
@@ -2088,15 +2154,13 @@ def peakdetect1d(
 
 
 def fit_energy_calibration(
-    pos: Union[List[float], np.ndarray],
-    vals: Union[List[float], np.ndarray],
+    pos: list[float] | np.ndarray,
+    vals: list[float] | np.ndarray,
     binwidth: float,
     binning: int,
-    ref_id: int = 0,
-    ref_energy: float = None,
-    t: Union[List[float], np.ndarray] = None,
+    ref_energy: float,
+    t: list[float] | np.ndarray = None,
     energy_scale: str = "kinetic",
-    verbose: bool = True,
     **kwds,
 ) -> dict:
     """Energy calibration by nonlinear least squares fitting of spectral landmarks on
@@ -2104,23 +2168,20 @@ def fit_energy_calibration(
     function d/(t-t0)**2.
 
     Args:
-        pos (Union[List[float], np.ndarray]): Positions of the spectral landmarks
+        pos (list[float] | np.ndarray): Positions of the spectral landmarks
             (e.g. peaks) in the EDCs.
-        vals (Union[List[float], np.ndarray]): Bias voltage value associated with
+        vals (list[float] | np.ndarray): Bias voltage value associated with
             each EDC.
         binwidth (float): Time width of each original TOF bin in ns.
         binning (int): Binning factor of the TOF values.
-        ref_id (int, optional): Reference dataset index. Defaults to 0.
-        ref_energy (float, optional): Energy value of the feature in the reference
-            trace (eV). required to output the calibration. Defaults to None.
-        t (Union[List[float], np.ndarray], optional): Array of TOF values. Required
+        ref_energy (float): Energy value of the feature in the reference
+            trace (eV).
+        t (list[float] | np.ndarray, optional): Array of TOF values. Required
             to calculate calibration trace. Defaults to None.
         energy_scale (str, optional): Direction of increasing energy scale.
 
             - **'kinetic'**: increasing energy with decreasing TOF.
             - **'binding'**: increasing energy with increasing TOF.
-        verbose (bool, optional): Option to print out diagnostic information.
-            Defaults to True.
         **kwds: keyword arguments:
 
             - **t0** (float): constrains and initial values for the fit parameter t0,
@@ -2137,14 +2198,6 @@ def fit_energy_calibration(
         - "axis": Fitted energy axis.
     """
     vals = np.asarray(vals)
-    nvals = vals.size
-
-    if ref_id >= nvals:
-        wn.warn(
-            "Reference index (refid) cannot be larger than the number of traces!\
-                Reset to the largest allowed number.",
-        )
-        ref_id = nvals - 1
 
     def residual(pars, time, data, binwidth, binning, energy_scale):
         model = tof2ev(
@@ -2160,8 +2213,14 @@ def residual(pars, time, data, binwidth, binning, energy_scale):
             return model
         return model - data
 
-    pars = Parameters()
     d_pars = kwds.pop("d", {})
+    t0_pars = kwds.pop("t0", {})
+    E0_pars = kwds.pop("E0", {})
+
+    if len(kwds) > 0:
+        raise TypeError(f"fit_energy_calibration() got unexpected keyword arguments {kwds.keys()}.")
+
+    pars = Parameters()
     pars.add(
         name="d",
         value=d_pars.get("value", 1),
@@ -2169,18 +2228,13 @@ def residual(pars, time, data, binwidth, binning, energy_scale):
         max=d_pars.get("max", np.inf),
         vary=d_pars.get("vary", True),
     )
-    t0_pars = kwds.pop("t0", {})
     pars.add(
         name="t0",
         value=t0_pars.get("value", 1e-6),
         min=t0_pars.get("min", -np.inf),
-        max=t0_pars.get(
-            "max",
-            (min(pos) - 1) * binwidth * 2**binning,
-        ),
+        max=t0_pars.get("max", (min(pos) - 1) * binwidth * binning),
         vary=t0_pars.get("vary", True),
     )
-    E0_pars = kwds.pop("E0", {})  # pylint: disable=invalid-name
     pars.add(
         name="E0",
         value=E0_pars.get("value", min(vals)),
@@ -2194,8 +2248,7 @@ def residual(pars, time, data, binwidth, binning, energy_scale):
         fcn_args=(pos, vals, binwidth, binning, energy_scale),
     )
     result = fit.leastsq()
-    if verbose:
-        report_fit(result)
+    logger.info(fit_report(result))
 
     # Construct the calibrating function
     pfunc = partial(
@@ -2213,23 +2266,21 @@ def residual(pars, time, data, binwidth, binning, energy_scale):
     ecalibdict["t0"] = result.params["t0"].value
     ecalibdict["E0"] = result.params["E0"].value
     ecalibdict["energy_scale"] = energy_scale
+    energy_offset = pfunc(-1 * ref_energy, pos[0])
+    ecalibdict["E0"] = -(energy_offset - vals[0])
 
-    if (ref_energy is not None) and (t is not None):
-        energy_offset = pfunc(-1 * ref_energy, pos[ref_id])
-        ecalibdict["axis"] = pfunc(-energy_offset, t)
-        ecalibdict["E0"] = -energy_offset
-        ecalibdict["refid"] = ref_id
+    if t is not None:
+        ecalibdict["axis"] = pfunc(ecalibdict["E0"], t)
 
     return ecalibdict
 
 
 def poly_energy_calibration(
-    pos: Union[List[float], np.ndarray],
-    vals: Union[List[float], np.ndarray],
+    pos: list[float] | np.ndarray,
+    vals: list[float] | np.ndarray,
+    ref_energy: float,
     order: int = 3,
-    ref_id: int = 0,
-    ref_energy: float = None,
-    t: Union[List[float], np.ndarray] = None,
+    t: list[float] | np.ndarray = None,
     aug: int = 1,
     method: str = "lstsq",
     energy_scale: str = "kinetic",
@@ -2244,22 +2295,21 @@ def poly_energy_calibration(
 
 
     Args:
-        pos (Union[List[float], np.ndarray]): Positions of the spectral landmarks
+        pos (list[float] | np.ndarray): Positions of the spectral landmarks
             (e.g. peaks) in the EDCs.
-        vals (Union[List[float], np.ndarray]): Bias voltage value associated with
+        vals (list[float] | np.ndarray): Bias voltage value associated with
             each EDC.
+        ref_energy (float): Energy value of the feature in the reference
+            trace (eV).
         order (int, optional): Polynomial order of the fitting function. Defaults to 3.
-        ref_id (int, optional): Reference dataset index. Defaults to 0.
-        ref_energy (float, optional): Energy value of the feature in the reference
-            trace (eV). required to output the calibration. Defaults to None.
-        t (Union[List[float], np.ndarray], optional): Array of TOF values. Required
+        t (list[float] | np.ndarray, optional): Array of TOF values. Required
             to calculate calibration trace. Defaults to None.
         aug (int, optional): Fitting dimension augmentation
             (1=no change, 2=double, etc). Defaults to 1.
         method (str, optional): Method for determining the energy calibration.
 
-            - **'lmfit'**: Energy calibration using lmfit and 1/t^2 form.
-            - **'lstsq'**, **'lsqr'**: Energy calibration using polynomial form..
+            - **'lstsq'**: Use ``numpy.linalg.lstsq``.
+            - **'lsqr'**: Use ``scipy.sparse.linalg.lsqr``
 
             Defaults to "lstsq".
         energy_scale (str, optional): Direction of increasing energy scale.
@@ -2267,6 +2317,8 @@ def poly_energy_calibration(
             - **'kinetic'**: increasing energy with decreasing TOF.
             - **'binding'**: increasing energy with increasing TOF.
 
+        **kwds: Keyword arguments passed to ``lstsq`` or ``lsqr``.
+
     Returns:
         dict: A dictionary of fitting parameters including the following,
 
@@ -2279,21 +2331,14 @@ def poly_energy_calibration(
     vals = np.asarray(vals)
     nvals = vals.size
 
-    if ref_id >= nvals:
-        wn.warn(
-            "Reference index (refid) cannot be larger than the number of traces!\
-                Reset to the largest allowed number.",
-        )
-        ref_id = nvals - 1
-
     # Top-to-bottom ordering of terms in the T matrix
-    termorder = np.delete(range(0, nvals, 1), ref_id)
+    termorder = np.delete(range(0, nvals, 1), 0)
     termorder = np.tile(termorder, aug)
     # Left-to-right ordering of polynomials in the T matrix
     polyorder = np.linspace(order, 1, order, dtype="int")
 
     # Construct the T (differential drift time) matrix, Tmat = Tmain - Tsec
-    t_main = np.array([pos[ref_id] ** p for p in polyorder])
+    t_main = np.array([pos[0] ** p for p in polyorder])
     # Duplicate to the same order as the polynomials
     t_main = np.tile(t_main, (aug * (nvals - 1), 1))
 
@@ -2305,12 +2350,12 @@ def poly_energy_calibration(
     t_mat = t_main - np.asarray(t_sec)
 
     # Construct the b vector (differential bias)
-    bvec = vals[ref_id] - np.delete(vals, ref_id)
+    bvec = vals[0] - np.delete(vals, 0)
     bvec = np.tile(bvec, aug)
 
     # Solve for the a vector (polynomial coefficients) using least squares
     if method == "lstsq":
-        sol = lstsq(t_mat, bvec, rcond=None)
+        sol = lstsq(t_mat, bvec, rcond=None, **kwds)
     elif method == "lsqr":
         sol = lsqr(t_mat, bvec, **kwds)
     poly_a = sol[0]
@@ -2325,12 +2370,10 @@ def poly_energy_calibration(
     ecalibdict["Tmat"] = t_mat
     ecalibdict["bvec"] = bvec
     ecalibdict["energy_scale"] = energy_scale
+    ecalibdict["E0"] = -(pfunc(-1 * ref_energy, pos[0]) + vals[0])
 
-    if ref_energy is not None and t is not None:
-        energy_offset = pfunc(-1 * ref_energy, pos[ref_id])
-        ecalibdict["axis"] = pfunc(-energy_offset, t)
-        ecalibdict["E0"] = -energy_offset
-        ecalibdict["refid"] = ref_id
+    if t is not None:
+        ecalibdict["axis"] = pfunc(-ecalibdict["E0"], t)
 
     return ecalibdict
 
@@ -2367,7 +2410,7 @@ def tof2ev(
 
     #         m_e/2 [eV]                      bin width [s]
     energy = (
-        2.84281e-12 * sign * (tof_distance / (t * binwidth * 2**binning - time_offset)) ** 2
+        2.84281e-12 * sign * (tof_distance / (t * binwidth * binning - time_offset)) ** 2
         + energy_offset
     )
 
@@ -2375,7 +2418,7 @@ def tof2ev(
 
 
 def tof2evpoly(
-    poly_a: Union[List[float], np.ndarray],
+    poly_a: list[float] | np.ndarray,
     energy_offset: float,
     t: float,
 ) -> float:
@@ -2383,7 +2426,7 @@ def tof2evpoly(
     conversion formula.
 
     Args:
-        poly_a (Union[List[float], np.ndarray]): Polynomial coefficients.
+        poly_a (list[float] | np.ndarray): Polynomial coefficients.
         energy_offset (float): Energy offset in eV.
         t (float): TOF value in bin number.
 
@@ -2417,5 +2460,5 @@ def tof2ns(
     Returns:
         float: Converted time in nanoseconds.
     """
-    val = t * 1e9 * binwidth * 2.0**binning
+    val = t * 1e9 * binwidth * binning
     return val
diff --git a/sed/calibrator/momentum.py b/src/sed/calibrator/momentum.py
similarity index 84%
rename from sed/calibrator/momentum.py
rename to src/sed/calibrator/momentum.py
index 4835c6b3..391270b0 100644
--- a/sed/calibrator/momentum.py
+++ b/src/sed/calibrator/momentum.py
@@ -1,14 +1,13 @@
 """sed.calibrator.momentum module. Code for momentum calibration and distortion
 correction. Mostly ported from https://github.com/mpes-kit/mpes.
 """
+from __future__ import annotations
+
 import itertools as it
 from copy import deepcopy
 from datetime import datetime
 from typing import Any
-from typing import Dict
-from typing import List
-from typing import Tuple
-from typing import Union
+from typing import Literal
 
 import bokeh.palettes as bp
 import bokeh.plotting as pbk
@@ -34,48 +33,62 @@
 from symmetrize import sym
 from symmetrize import tps
 
+from sed.core.logging import set_verbosity
+from sed.core.logging import setup_logging
+
+# Configure logging
+logger = setup_logging("momentum")
+
 
 class MomentumCorrector:
     """
     Momentum distortion correction and momentum calibration workflow functions.
 
     Args:
-        data (Union[xr.DataArray, np.ndarray], optional): Multidimensional hypervolume
+        data (xr.DataArray | np.ndarray, optional): Multidimensional hypervolume
             containing the data. Defaults to None.
-        bin_ranges (List[Tuple], optional): Binning ranges of the data volume, if
+        bin_ranges (list[tuple], optional): Binning ranges of the data volume, if
             provided as np.ndarray. Defaults to None.
         rotsym (int, optional): Rotational symmetry of the data. Defaults to 6.
         config (dict, optional): Config dictionary. Defaults to None.
+        verbose (bool, optional): Option to print out diagnostic information.
+            Defaults to True.
     """
 
     def __init__(
         self,
-        data: Union[xr.DataArray, np.ndarray] = None,
-        bin_ranges: List[Tuple] = None,
+        data: xr.DataArray | np.ndarray = None,
+        bin_ranges: list[tuple] = None,
         rotsym: int = 6,
         config: dict = None,
+        verbose: bool = True,
     ):
         """Constructor of the MomentumCorrector class.
 
         Args:
-            data (Union[xr.DataArray, np.ndarray], optional): Multidimensional
+            data (xr.DataArray | np.ndarray, optional): Multidimensional
                 hypervolume containing the data. Defaults to None.
-            bin_ranges (List[Tuple], optional): Binning ranges of the data volume,
+            bin_ranges (list[tuple], optional): Binning ranges of the data volume,
                 if provided as np.ndarray. Defaults to None.
             rotsym (int, optional): Rotational symmetry of the data. Defaults to 6.
             config (dict, optional): Config dictionary. Defaults to None.
+            verbose (bool, optional): Option to print out diagnostic information.
+                Defaults to True.
         """
         if config is None:
             config = {}
 
         self._config = config
 
+        self._verbose = verbose
+        set_verbosity(logger, self._verbose)
+
         self.image: np.ndarray = None
         self.img_ndim: int = None
         self.slice: np.ndarray = None
         self.slice_corrected: np.ndarray = None
         self.slice_transformed: np.ndarray = None
-        self.bin_ranges: List[Tuple] = self._config["momentum"].get("bin_ranges", [])
+        self.bin_ranges: list[tuple] = self._config["momentum"].get("bin_ranges", [])
 
         if data is not None:
             self.load_data(data=data, bin_ranges=bin_ranges)
@@ -90,7 +103,7 @@ def __init__(
         self.include_center: bool = False
         self.use_center: bool = False
         self.pouter: np.ndarray = None
-        self.pcent: Tuple[float, ...] = None
+        self.pcent: tuple[float, float] = None
         self.pouter_ord: np.ndarray = None
         self.prefs: np.ndarray = None
         self.ptargs: np.ndarray = None
@@ -106,20 +119,39 @@ def __init__(
         self.cdeform_field_bkp: np.ndarray = None
         self.inverse_dfield: np.ndarray = None
         self.dfield_updated: bool = False
-        self.transformations: Dict[str, Any] = self._config["momentum"].get("transformations", {})
-        self.correction: Dict[str, Any] = self._config["momentum"].get("correction", {})
-        self.adjust_params: Dict[str, Any] = {}
-        self.calibration: Dict[str, Any] = self._config["momentum"].get("calibration", {})
-
-        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.transformations: dict[str, Any] = self._config["momentum"].get("transformations", {})
+        self.correction: dict[str, Any] = self._config["momentum"].get("correction", {})
+        self.adjust_params: dict[str, Any] = {}
+        self.calibration: dict[str, Any] = self._config["momentum"].get("calibration", {})
+
+        self.x_column = self._config["dataframe"]["columns"]["x"]
+        self.y_column = self._config["dataframe"]["columns"]["y"]
+        self.corrected_x_column = self._config["dataframe"]["columns"]["corrected_x"]
+        self.corrected_y_column = self._config["dataframe"]["columns"]["corrected_y"]
+        self.kx_column = self._config["dataframe"]["columns"]["kx"]
+        self.ky_column = self._config["dataframe"]["columns"]["ky"]
 
         self._state: int = 0
 
+    @property
+    def verbose(self) -> bool:
+        """Accessor to the verbosity flag.
+
+        Returns:
+            bool: Verbosity flag.
+        """
+        return self._verbose
+
+    @verbose.setter
+    def verbose(self, verbose: bool):
+        """Setter for the verbosity.
+
+        Args:
+            verbose (bool): Option to turn on verbose output. Sets loglevel to INFO.
+        """
+        self._verbose = verbose
+        set_verbosity(logger, self._verbose)
+
     @property
     def features(self) -> dict:
         """Dictionary of detected features for the symmetrization process.
@@ -153,15 +185,15 @@ def symscores(self) -> dict:
 
     def load_data(
         self,
-        data: Union[xr.DataArray, np.ndarray],
-        bin_ranges: List[Tuple] = None,
+        data: xr.DataArray | np.ndarray,
+        bin_ranges: list[tuple] = None,
     ):
         """Load binned data into the momentum calibrator class
 
         Args:
-            data (Union[xr.DataArray, np.ndarray]):
+            data (xr.DataArray | np.ndarray):
                 2D or 3D data array, either as np.ndarray or xr.DataArray.
-            bin_ranges (List[Tuple], optional):
+            bin_ranges (list[tuple], optional):
                 Binning ranges. Needs to be provided in case the data are given
                 as np.ndarray. Otherwise, they are determined from the coords of
                 the xr.DataArray. Defaults to None.
@@ -267,9 +299,7 @@ def apply_fun(apply: bool):  # noqa: ARG001
             axmax = np.max(self.slice, axis=(0, 1))
             if axmin < axmax:
                 img.set_clim(axmin, axmax)
-            ax.set_title(
-                f"Plane[{start}:{stop}]",
-            )
+            ax.set_title(f"Plane[{start}:{stop}]")
             fig.canvas.draw_idle()
 
             plane_slider.close()
@@ -287,13 +317,13 @@ def apply_fun(apply: bool):  # noqa: ARG001
 
     def select_slice(
         self,
-        selector: Union[slice, List[int], int],
+        selector: slice | list[int] | int,
         axis: int = 2,
     ):
         """Select (hyper)slice from a (hyper)volume.
 
         Args:
-            selector (Union[slice, List[int], int]):
+            selector (slice | list[int] | int):
                 Selector along the specified axis to extract the slice (image). Use
                 the construct slice(start, stop, step) to select a range of images
                 and sum them. Use an integer to specify only a particular slice.
@@ -312,6 +342,8 @@ def select_slice(
             if self.slice is not None:
                 self.slice_corrected = self.slice_transformed = self.slice
 
+            logger.debug(f"Selected energy slice {selector} for momentum correction.")
+
         elif self.img_ndim == 2:
             raise ValueError("Input image dimension is already 2!")
 
@@ -321,7 +353,7 @@ def add_features(
         direction: str = "ccw",
         rotsym: int = 6,
         symscores: bool = True,
-        **kwds,
+        symtype: str = "rotation",
     ):
         """Add features as reference points provided as np.ndarray. If provided,
         detects the center of the points and orders the points.
@@ -335,9 +367,7 @@ def add_features(
                 Direction for ordering the points. Defaults to "ccw".
             symscores (bool, optional):
                 Option to calculate symmetry scores. Defaults to False.
-            **kwds: Keyword arguments.
-
-                - **symtype** (str): Type of symmetry scores to calculate
+            symtype (str, optional): Type of symmetry scores to calculate
                   if symscores is True. Defaults to "rotation".
 
         Raises:
@@ -378,7 +408,6 @@ def add_features(
             self.calc_geometric_distances()
 
         if symscores is True:
-            symtype = kwds.pop("symtype", "rotation")
             self.csm_original = self.calc_symmetry_scores(symtype=symtype)
 
         if self.rotsym == 6 and self.pcent is not None:
@@ -411,7 +440,8 @@ def feature_extract(
             symscores (bool, optional):
                 Option for calculating symmetry scores. Defaults to True.
             **kwds:
-                Extra keyword arguments for ``symmetrize.pointops.peakdetect2d()``.
+                Extra keyword arguments for ``symmetrize.pointops.peakdetect2d()`` and
+                ``add_features()``.
 
         Raises:
             NotImplementedError:
@@ -423,6 +453,15 @@ def feature_extract(
             else:
                 raise ValueError("No image loaded for feature extraction!")
 
+        # split off config keywords
+        feature_kwds = {
+            key: value
+            for key, value in kwds.items()
+            if key in self.add_features.__code__.co_varnames
+        }
+        for key in feature_kwds.keys():
+            del kwds[key]
+
         if feature_type == "points":
             # Detect the point landmarks
             self.peaks = po.peakdetect2d(image, **kwds)
@@ -432,7 +471,7 @@ def feature_extract(
                 direction=direction,
                 rotsym=rotsym,
                 symscores=symscores,
-                **kwds,
+                **feature_kwds,
             )
         else:
             raise NotImplementedError
@@ -465,7 +504,7 @@ def feature_select(
             apply (bool, optional): Option to directly store the features in the class.
                 Defaults to False.
             **kwds:
-                Extra keyword arguments for ``symmetrize.pointops.peakdetect2d()``.
+                Keyword arguments for ``add_features``.
 
         Raises:
             ValueError: If no valid image is found from which to ge the coordinates.
@@ -507,8 +546,8 @@ def update_point_pos(
             features[point_no][0] = point_x
             features[point_no][1] = point_y
 
-            markers[point_no].set_xdata(point_x)
-            markers[point_no].set_ydata(point_y)
+            markers[point_no].set_xdata([point_x])
+            markers[point_no].set_ydata([point_y])
 
         point_no_input = ipw.Dropdown(
             options=range(features.shape[0]),
@@ -544,11 +583,7 @@ def apply_func(apply: bool):  # noqa: ARG001
 
             fig.canvas.draw_idle()
 
-            self.add_features(
-                features=features,
-                rotsym=rotsym,
-                **kwds,
-            )
+            self.add_features(features=features, rotsym=rotsym, **kwds)
 
         apply_button = ipw.Button(description="apply")
         display(apply_button)
@@ -594,8 +629,7 @@ def spline_warp_estimate(
         use_center: bool = None,
         fixed_center: bool = True,
         interp_order: int = 1,
-        ascale: Union[float, list, tuple, np.ndarray] = None,
-        verbose: bool = True,
+        ascale: float | list | tuple | np.ndarray = None,
         **kwds,
     ) -> np.ndarray:
         """Estimate the spline deformation field using thin plate spline registration.
@@ -612,15 +646,13 @@ def spline_warp_estimate(
             interp_order (int, optional):
                 Order of interpolation (see ``scipy.ndimage.map_coordinates()``).
                 Defaults to 1.
-            ascale: (Union[float, np.ndarray], optional): Scale parameter determining a relative
-                scale for each symmetry feature. If provided as single float, rotsym has to be 4.
-                This parameter describes the relative scaling between the two orthogonal symmetry
-                directions (for an orthorhombic system). This requires the correction points to be
-                located along the principal axes (X/Y points of the Brillouin zone). Otherwise, an
-                array with ``rotsym`` elements is expected, containing relative scales for each
-                feature. Defaults to an array of equal scales.
-            verbose (bool, optional): Option to report the used landmarks for correction.
-                Defaults to True.
+            ascale: (float | list | tuple | np.ndarray, optional): Scale parameter determining a
+                relative scale for each symmetry feature. If provided as single float, rotsym has
+                to be 4. This parameter describes the relative scaling between the two orthogonal
+                symmetry directions (for an orthorhombic system). This requires the correction
+                points to be located along the principal axes (X/Y points of the Brillouin zone).
+                Otherwise, an array with ``rotsym`` elements is expected, containing relative
+                scales for each feature. Defaults to an array of equal scales.
             **kwds: keyword arguments:
 
                 - **landmarks**: (list/array): Landmark positions (row, column) used
@@ -631,6 +663,9 @@ def spline_warp_estimate(
                 - **new_centers**: (dict): User-specified center positions for the
                   reference and target sets. {'lmkcenter': (row, col),
                   'targcenter': (row, col)}
+
+                Additional keywords are passed to ``tpsWarping()``.
+
         Returns:
             np.ndarray: The corrected image.
         """
@@ -644,8 +679,7 @@ def spline_warp_estimate(
         if self.pouter_ord is None:
             if self.pouter is not None:
                 self.pouter_ord = po.pointset_order(self.pouter)
-                self.correction["creation_date"] = datetime.now().timestamp()
-                self.correction["creation_date"] = datetime.now().timestamp()
+                self.correction["creation_date"] = datetime.now()
             else:
                 try:
                     features = np.asarray(
@@ -659,31 +693,26 @@ def spline_warp_estimate(
                     if ascale is not None:
                         ascale = np.asarray(ascale)
 
-                    if verbose:
-                        if "creation_date" in self.correction:
-                            datestring = datetime.fromtimestamp(
-                                self.correction["creation_date"],
-                            ).strftime(
-                                "%m/%d/%Y, %H:%M:%S",
-                            )
-                            print(
-                                "No landmarks defined, using momentum correction parameters "
-                                f"generated on {datestring}",
-                            )
-                        else:
-                            print(
-                                "No landmarks defined, using momentum correction parameters "
-                                "from config.",
-                            )
+                    if "creation_date" in self.correction:
+                        datestring = self.correction["creation_date"].strftime("%m/%d/%Y, %H:%M:%S")
+                        logger.info(
+                            "No landmarks defined, using momentum correction parameters "
+                            f"generated on {datestring}",
+                        )
+                    else:
+                        logger.info(
+                            "No landmarks defined, using momentum correction parameters "
+                            "from config.",
+                        )
                 except KeyError as exc:
                     raise ValueError(
                         "No valid landmarks defined, and no landmarks found in configuration!",
                     ) from exc
 
-                self.add_features(features=features, rotsym=rotsym, include_center=include_center)
+                self.add_features(features=features, rotsym=rotsym)
 
         else:
-            self.correction["creation_date"] = datetime.now().timestamp()
+            self.correction["creation_date"] = datetime.now()
 
         if ascale is not None:
             if isinstance(ascale, (int, float, np.floating, np.integer)):
@@ -715,6 +744,7 @@ def spline_warp_estimate(
 
         self.prefs = kwds.pop("landmarks", self.pouter_ord)
         self.ptargs = kwds.pop("targets", [])
+        newcenters = kwds.pop("new_centers", {})
 
         # Generate the target point set
         if not self.ptargs:
@@ -736,7 +766,6 @@ def spline_warp_estimate(
                 self.ptargs = np.column_stack((self.ptargs.T, self.pcent)).T
 
             else:  # Add different centers to the reference and target sets
-                newcenters = kwds.pop("new_centers", {})
                 self.prefs = np.column_stack(
                     (self.prefs.T, newcenters["lmkcenter"]),
                 ).T
@@ -784,11 +813,13 @@ def spline_warp_estimate(
         if self.slice is not None:
             self.slice_corrected = corrected_image
 
-        if verbose:
-            print("Calculated thin spline correction based on the following landmarks:")
-            print(f"pouter: {self.pouter}")
-            if use_center:
-                print(f"pcent: {self.pcent}")
+        log_str = (
+            "Calculated thin spline correction based on the following landmarks:\n"
+            f"pouter_ord: {self.pouter_ord}"
+        )
+        if use_center:
+            log_str += f"\npcent: {self.pcent}"
+        logger.info(log_str)
 
         return corrected_image
 
@@ -833,6 +864,10 @@ def reset_deformation(self, **kwds):
         """
         image = kwds.pop("image", self.slice)
         coordtype = kwds.pop("coordtype", "cartesian")
+
+        if len(kwds) > 0:
+            raise TypeError(f"reset_deformation() got unexpected keyword arguments {kwds.keys()}.")
+
         coordmat = sym.coordinate_matrix_2D(
             image,
             coordtype=coordtype,
@@ -896,7 +931,12 @@ def coordinate_transform(
             mapkwds (dict, optional): Additional arguments passed to
                 ``scipy.ndimage.map_coordinates()``. Defaults to None.
             **kwds: keyword arguments.
-                Additional arguments in specific deformation field.
+
+                - **image**: Image to use. Defaults to self.slice.
+                - **stackaxis**: Stacking axis for coordinate transformation matrices.
+                  Defaults to 0.
+
+                Additional arguments are passed to the specific deformation field generators.
                 See ``symmetrize.sym`` module.
         Returns:
             np.ndarray: The corrected image.
@@ -1028,10 +1068,9 @@ def coordinate_transform(
 
     def pose_adjustment(
         self,
-        transformations: Dict[str, Any] = None,
+        transformations: dict[str, Any] = None,
         apply: bool = False,
         reset: bool = True,
-        verbose: bool = True,
         **kwds,
     ):
         """Interactive panel to adjust transformations that are applied to the image.
@@ -1046,8 +1085,6 @@ def pose_adjustment(
                 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.
             **kwds: Keyword parameters defining defaults for the transformations:
 
                 - **scale** (float): Initial value of the scaling slider.
@@ -1085,14 +1122,18 @@ def pose_adjustment(
             transformations = deepcopy(self.transformations)
 
         if len(kwds) > 0:
-            for key, value in kwds.items():
-                transformations[key] = value
+            for key in ["scale", "xtrans", "ytrans", "angle"]:
+                if key in kwds:
+                    transformations[key] = kwds.pop(key)
 
-        elif "creation_date" in transformations and verbose:
-            datestring = datetime.fromtimestamp(transformations["creation_date"]).strftime(
-                "%m/%d/%Y, %H:%M:%S",
-            )
-            print(f"Using transformation parameters generated on {datestring}")
+            if len(kwds) > 0:
+                raise TypeError(
+                    f"pose_adjustment() got unexpected keyword arguments {kwds.keys()}.",
+                )
+
+        elif "creation_date" in transformations:
+            datestring = transformations["creation_date"].strftime("%m/%d/%Y, %H:%M:%S")
+            logger.info(f"Using transformation parameters generated on {datestring}")
 
         def update(scale: float, xtrans: float, ytrans: float, angle: float):
             transformed_image = source_image
@@ -1179,9 +1220,7 @@ def apply_func(apply: bool):  # noqa: ARG001
                     yscale=transformations["scale"],
                     keep=True,
                 )
-                if verbose:
-                    with results_box:
-                        print(f"Applied scaling with scale={transformations['scale']}.")
+                logger.info(f"Applied scaling with scale={transformations['scale']}.")
             if transformations.get("xtrans", 0) != 0 or transformations.get("ytrans", 0) != 0:
                 self.coordinate_transform(
                     transform_type="translation",
@@ -1189,12 +1228,10 @@ def apply_func(apply: bool):  # noqa: ARG001
                     ytrans=transformations.get("ytrans", 0),
                     keep=True,
                 )
-                if verbose:
-                    with results_box:
-                        print(
-                            f"Applied translation with (xtrans={transformations.get('xtrans', 0)},",
-                            f"ytrans={transformations.get('ytrans', 0)}).",
-                        )
+                logger.info(
+                    f"Applied translation with (xtrans={transformations.get('xtrans', 0)}, "
+                    f"ytrans={transformations.get('ytrans', 0)}).",
+                )
             if transformations.get("angle", 0) != 0:
                 self.coordinate_transform(
                     transform_type="rotation",
@@ -1202,9 +1239,7 @@ def apply_func(apply: bool):  # noqa: ARG001
                     center=center,
                     keep=True,
                 )
-                if verbose:
-                    with results_box:
-                        print(f"Applied rotation with angle={transformations['angle']}.")
+                logger.info(f"Applied rotation with angle={transformations['angle']}.")
 
                 display(results_box)
 
@@ -1217,13 +1252,13 @@ def apply_func(apply: bool):  # noqa: ARG001
                 fig.canvas.draw_idle()
 
             if transformations != self.transformations:
-                transformations["creation_date"] = datetime.now().timestamp()
+                transformations["creation_date"] = datetime.now()
                 self.transformations = transformations
 
-            if verbose:
+            if self._verbose:
                 plt.figure()
                 subs = 20
-                plt.title("Deformation field")
+                plt.title("Final Deformation field")
                 plt.scatter(
                     self.rdeform_field[::subs, ::subs].ravel(),
                     self.cdeform_field[::subs, ::subs].ravel(),
@@ -1257,12 +1292,12 @@ def calc_inverse_dfield(self):
 
         return self.inverse_dfield
 
-    def view(  # pylint: disable=dangerous-default-value
+    def view(
         self,
         image: np.ndarray = None,
-        origin: str = "lower",
+        origin: Literal["upper", "lower"] = "lower",
         cmap: str = "terrain_r",
-        figsize: Tuple[int, int] = (4, 4),
+        figsize: tuple[int, int] = (4, 4),
         points: dict = None,
         annotated: bool = False,
         backend: str = "matplotlib",
@@ -1270,7 +1305,7 @@ def view(  # pylint: disable=dangerous-default-value
         scatterkwds: dict = {},
         cross: bool = False,
         crosshair: bool = False,
-        crosshair_radii: List[int] = [50, 100, 150],
+        crosshair_radii: list[int] = [50, 100, 150],
         crosshair_thickness: int = 1,
         **kwds,
     ):
@@ -1278,10 +1313,10 @@ def view(  # pylint: disable=dangerous-default-value
 
         Args:
             image (np.ndarray, optional): The image to plot. Defaults to self.slice.
-            origin (str, optional): Figure origin specification ('lower' or 'upper').
-                Defaults to "lower".
+            origin (Literal["upper", "lower"], optional): Figure origin specification
+                ('lower' or 'upper'). Defaults to "lower".
             cmap (str, optional):  Colormap specification. Defaults to "terrain_r".
-            figsize (Tuple[int, int], optional): Figure size. Defaults to (4, 4).
+            figsize (tuple[int, int], optional): Figure size. Defaults to (4, 4).
             points (dict, optional): Points for annotation. Defaults to None.
             annotated (bool, optional): Option to add annotation. Defaults to False.
             backend (str, optional): Visualization backend specification. Defaults to
@@ -1298,7 +1333,7 @@ def view(  # pylint: disable=dangerous-default-value
                 self.pcent. Defaults to False.
             crosshair (bool, optional): Display option to plot circles around center
                 self.pcent. Works only in bokeh backend. Defaults to False.
-            crosshair_radii (List[int], optional): Pixel radii of circles to plot when
+            crosshair_radii (list[int], optional): Pixel radii of circles to plot when
                 crosshair option is activated. Defaults to [50, 100, 150].
             crosshair_thickness (int, optional): Thickness of crosshair circles.
                 Defaults to 1.
@@ -1314,10 +1349,19 @@ def view(  # pylint: disable=dangerous-default-value
 
         if annotated:
             tsr, tsc = kwds.pop("textshift", (3, 3))
-            txtsize = kwds.pop("textsize", 12)
+            txtsize = kwds.pop("textsize", 10)
+
+        title = kwds.pop("title", "")
+
+        # Handle unexpected kwds:
+        handled_kwds = {"figsize"}
+        if not set(kwds.keys()).issubset(handled_kwds):
+            raise TypeError(
+                f"view() got unexpected keyword arguments {set(kwds.keys()) - handled_kwds}.",
+            )
 
         if backend == "matplotlib":
-            fig, ax = plt.subplots(figsize=figsize)
+            _, ax = plt.subplots(figsize=figsize)
             ax.imshow(image.T, origin=origin, cmap=cmap, **imkwds)
 
             if cross:
@@ -1327,15 +1371,12 @@ def view(  # pylint: disable=dangerous-default-value
 
             # Add annotation to the figure
             if annotated:
-                for (
-                    p_keys,  # pylint: disable=unused-variable
-                    p_vals,
-                ) in points.items():
+                for p_keys, p_vals in points.items():
                     try:
-                        ax.scatter(p_vals[:, 0], p_vals[:, 1], **scatterkwds)
+                        ax.scatter(p_vals[:, 0], p_vals[:, 1], s=15, **scatterkwds)
                     except IndexError:
                         try:
-                            ax.scatter(p_vals[0], p_vals[1], **scatterkwds)
+                            ax.scatter(p_vals[0], p_vals[1], s=15, **scatterkwds)
                         except IndexError:
                             pass
 
@@ -1348,15 +1389,21 @@ def view(  # pylint: disable=dangerous-default-value
                                 fontsize=txtsize,
                             )
 
+            if crosshair and self.pcent is not None:
+                for radius in crosshair_radii:
+                    circle = plt.Circle(self.pcent, radius, color="k", fill=False)
+                    ax.add_patch(circle)
+
+            ax.set_title(title)
+
         elif backend == "bokeh":
             output_notebook(hide_banner=True)
             colors = it.cycle(ColorCycle[10])
             ttp = [("(x, y)", "($x, $y)")]
-            figsize = kwds.pop("figsize", (320, 300))
             palette = cm2palette(cmap)  # Retrieve palette colors
             fig = pbk.figure(
-                width=figsize[0],
-                height=figsize[1],
+                width=figsize[0] * 100,
+                height=figsize[1] * 100,
                 tooltips=ttp,
                 x_range=(0, num_rows),
                 y_range=(0, num_cols),
@@ -1409,11 +1456,11 @@ def view(  # pylint: disable=dangerous-default-value
 
     def select_k_range(
         self,
-        point_a: Union[np.ndarray, List[int]] = None,
-        point_b: Union[np.ndarray, List[int]] = None,
+        point_a: np.ndarray | list[int] = None,
+        point_b: np.ndarray | list[int] = None,
         k_distance: float = None,
-        k_coord_a: Union[np.ndarray, List[float]] = None,
-        k_coord_b: Union[np.ndarray, List[float]] = np.array([0.0, 0.0]),
+        k_coord_a: np.ndarray | list[float] = None,
+        k_coord_b: np.ndarray | list[float] = np.array([0.0, 0.0]),
         equiscale: bool = True,
         apply: bool = False,
     ):
@@ -1423,16 +1470,16 @@ def select_k_range(
         equiscale option for details on the specifications of point coordinates.
 
         Args:
-            point_a (Union[np.ndarray, List[int]], optional): Pixel coordinates of the
+            point_a (np.ndarray | list[int], optional): Pixel coordinates of the
                 symmetry point a.
-            point_b (Union[np.ndarray, List[int]], optional): Pixel coordinates of the
+            point_b (np.ndarray | list[int], optional): Pixel coordinates of the
                 symmetry point b. Defaults to the center pixel of the image, defined by
                 config["momentum"]["center_pixel"].
             k_distance (float, optional): The known momentum space distance between the
                 two symmetry points.
-            k_coord_a (Union[np.ndarray, List[float]], optional): Momentum coordinate
+            k_coord_a (np.ndarray | list[float], optional): Momentum coordinate
                 of the symmetry points a. Only valid if equiscale=False.
-            k_coord_b (Union[np.ndarray, List[float]], optional): Momentum coordinate
+            k_coord_b (np.ndarray | list[float], optional): Momentum coordinate
                 of the symmetry points b. Only valid if equiscale=False. Defaults to
                 the k-space center np.array([0.0, 0.0]).
             equiscale (bool, optional): Option to adopt equal scale along both the x
@@ -1485,10 +1532,10 @@ def update(
             k_distance: float,  # noqa: ARG001
         ):
             fig.canvas.draw_idle()
-            marker_a.set_xdata(point_a_x)
-            marker_a.set_ydata(point_a_y)
-            marker_b.set_xdata(point_b_x)
-            marker_b.set_ydata(point_b_y)
+            marker_a.set_xdata([point_a_x])
+            marker_a.set_ydata([point_a_y])
+            marker_b.set_xdata([point_b_x])
+            marker_b.set_ydata([point_b_y])
 
         point_a_input_x = ipw.IntText(point_a[0])
         point_a_input_y = ipw.IntText(point_a[1])
@@ -1559,11 +1606,11 @@ def apply_func(apply: bool):  # noqa: ARG001
 
     def calibrate(
         self,
-        point_a: Union[np.ndarray, List[int]],
-        point_b: Union[np.ndarray, List[int]],
+        point_a: np.ndarray | list[int],
+        point_b: np.ndarray | list[int],
         k_distance: float = None,
-        k_coord_a: Union[np.ndarray, List[float]] = None,
-        k_coord_b: Union[np.ndarray, List[float]] = np.array([0.0, 0.0]),
+        k_coord_a: np.ndarray | list[float] = None,
+        k_coord_b: np.ndarray | list[float] = np.array([0.0, 0.0]),
         equiscale: bool = True,
         image: np.ndarray = None,
     ) -> dict:
@@ -1574,16 +1621,16 @@ def calibrate(
         of point coordinates.
 
         Args:
-            point_a (Union[np.ndarray, List[int]], optional): Pixel coordinates of the
+            point_a (np.ndarray | list[int], optional): Pixel coordinates of the
                 symmetry point a.
-            point_b (Union[np.ndarray, List[int]], optional): Pixel coordinates of the
+            point_b (np.ndarray | list[int], optional): Pixel coordinates of the
                 symmetry point b. Defaults to the center pixel of the image, defined by
                 config["momentum"]["center_pixel"].
             k_distance (float, optional): The known momentum space distance between the
                 two symmetry points.
-            k_coord_a (Union[np.ndarray, List[float]], optional): Momentum coordinate
+            k_coord_a (np.ndarray | list[float], optional): Momentum coordinate
                 of the symmetry points a. Only valid if equiscale=False.
-            k_coord_b (Union[np.ndarray, List[float]], optional): Momentum coordinate
+            k_coord_b (np.ndarray | list[float], optional): Momentum coordinate
                 of the symmetry points b. Only valid if equiscale=False. Defaults to
                 the k-space center np.array([0.0, 0.0]).
             equiscale (bool, optional): Option to adopt equal scale along both the x
@@ -1635,6 +1682,12 @@ def calibrate(
             pixel_distance = norm(point_a - point_b)
             # Calculate the pixel to momentum conversion factor
             xratio = yratio = k_distance / pixel_distance
+            logger.debug(
+                f"Momentum calibration performed using the following parameters:\n"
+                f"point_a={point_a}\n"
+                f"point_b={point_b}\n"
+                f"k_distance={k_distance}",
+            )
 
         else:
             assert k_coord_a is not None
@@ -1645,6 +1698,13 @@ def calibrate(
             # Calculate the column- and row-wise conversion factor
             xratio = (kxa - kxb) / (point_a[0] - point_b[0])
             yratio = (kya - kyb) / (point_a[1] - point_b[1])
+            logger.debug(
+                f"Momentum calibration performed using the following parameters:\n"
+                f"point_a={point_a}\n"
+                f"point_b={point_b}\n"
+                f"k_coord_a={k_coord_a}\n"
+                f"k_coord_b={k_coord_b}",
+            )
 
         k_row = rowdist * xratio + k_coord_b[0]
         k_col = coldist * yratio + k_coord_b[1]
@@ -1654,7 +1714,7 @@ def calibrate(
 
         # Assemble into return dictionary
         self.calibration = {}
-        self.calibration["creation_date"] = datetime.now().timestamp()
+        self.calibration["creation_date"] = datetime.now()
         self.calibration["kx_axis"] = k_row
         self.calibration["ky_axis"] = k_col
         self.calibration["grid"] = (k_rowgrid, k_colgrid)
@@ -1676,42 +1736,31 @@ def calibrate(
 
     def apply_corrections(
         self,
-        df: Union[pd.DataFrame, dask.dataframe.DataFrame],
+        df: pd.DataFrame | dask.dataframe.DataFrame,
         x_column: str = None,
         y_column: str = None,
         new_x_column: str = None,
         new_y_column: str = None,
-        verbose: bool = True,
-        **kwds,
-    ) -> Tuple[Union[pd.DataFrame, dask.dataframe.DataFrame], dict]:
+    ) -> tuple[pd.DataFrame | dask.dataframe.DataFrame, dict]:
         """Calculate and replace the X and Y values with their distortion-corrected
         version.
 
         Args:
-            df (Union[pd.DataFrame, dask.dataframe.DataFrame]): Dataframe to apply
+            df (pd.DataFrame | dask.dataframe.DataFrame): Dataframe to apply
                 the distortion correction to.
             x_column (str, optional): Label of the 'X' column before momentum
-                distortion correction. Defaults to config["momentum"]["x_column"].
+                distortion correction. Defaults to config["dataframe"]["columns"]["x"].
             y_column (str, optional): Label of the 'Y' column before momentum
-                distortion correction. Defaults to config["momentum"]["y_column"].
+                distortion correction. Defaults to config["dataframe"]["columns"]["y"].
             new_x_column (str, optional): Label of the 'X' column after momentum
                 distortion correction.
-                Defaults to config["momentum"]["corrected_x_column"].
+                Defaults to config["dataframe"]["columns"]["corrected_x"].
             new_y_column (str, optional): Label of the 'Y' column after momentum
                 distortion correction.
-                Defaults to config["momentum"]["corrected_y_column"].
-            verbose (bool, optional): Option to report the used landmarks for correction.
-                Defaults to True.
-            **kwds: Keyword arguments:
-
-                - **dfield**: Inverse dfield
-                - **cdeform_field**, **rdeform_field**: Column- and row-wise forward
-                  deformation fields.
-
-                Additional keyword arguments are passed to ``apply_dfield``.
+                Defaults to config["dataframe"]["columns"]["corrected_y"].
 
         Returns:
-            Tuple[Union[pd.DataFrame, dask.dataframe.DataFrame], dict]: Dataframe with
+            tuple[pd.DataFrame | dask.dataframe.DataFrame, dict]: Dataframe with
             added columns and momentum correction metadata dictionary.
         """
         if x_column is None:
@@ -1729,7 +1778,7 @@ def apply_corrections(
                 if self.correction or self.transformations:
                     if self.correction:
                         # Generate spline warp from class features or config
-                        self.spline_warp_estimate(verbose=verbose)
+                        self.spline_warp_estimate()
                     if self.transformations:
                         # Apply config pose adjustments
                         self.pose_adjustment(apply=True)
@@ -1752,7 +1801,6 @@ def apply_corrections(
             new_x_column=new_x_column,
             new_y_column=new_y_column,
             detector_ranges=self.detector_ranges,
-            **kwds,
         )
 
         metadata = self.gather_correction_metadata()
@@ -1765,7 +1813,7 @@ def gather_correction_metadata(self) -> dict:
         Returns:
             dict: generated correction metadata dictionary.
         """
-        metadata: Dict[Any, Any] = {}
+        metadata: dict[Any, Any] = {}
         if len(self.correction) > 0:
             metadata["correction"] = self.correction
             metadata["correction"]["applied"] = True
@@ -1777,7 +1825,7 @@ def gather_correction_metadata(self) -> dict:
                 pass
         if len(self.adjust_params) > 0:
             metadata["registration"] = self.adjust_params
-            metadata["registration"]["creation_date"] = datetime.now().timestamp()
+            metadata["registration"]["creation_date"] = datetime.now()
             metadata["registration"]["applied"] = True
             metadata["registration"]["depends_on"] = (
                 "/entry/process/registration/transformations/rot_z"
@@ -1835,36 +1883,39 @@ def gather_correction_metadata(self) -> dict:
 
     def append_k_axis(
         self,
-        df: Union[pd.DataFrame, dask.dataframe.DataFrame],
+        df: pd.DataFrame | dask.dataframe.DataFrame,
         x_column: str = None,
         y_column: str = None,
         new_x_column: str = None,
         new_y_column: str = None,
         calibration: dict = None,
+        suppress_output: bool = False,
         **kwds,
-    ) -> Tuple[Union[pd.DataFrame, dask.dataframe.DataFrame], dict]:
+    ) -> tuple[pd.DataFrame | dask.dataframe.DataFrame, dict]:
         """Calculate and append the k axis coordinates (kx, ky) to the events dataframe.
 
         Args:
-            df (Union[pd.DataFrame, dask.dataframe.DataFrame]): Dataframe to apply the
+            df (pd.DataFrame | dask.dataframe.DataFrame): Dataframe to apply the
                 distortion correction to.
             x_column (str, optional): Label of the source 'X' column.
-                Defaults to config["momentum"]["corrected_x_column"] or
-                config["momentum"]["x_column"] (whichever is present).
+                Defaults to config["dataframe"]["columns"]["corrected_x"] or
+                config["dataframe"]["columns"]["x"] (whichever is present).
             y_column (str, optional): Label of the source 'Y' column.
-                Defaults to config["momentum"]["corrected_y_column"] or
-                config["momentum"]["y_column"] (whichever is present).
+                Defaults to config["dataframe"]["columns"]["corrected_y"] or
+                config["dataframe"]["columns"]["y"] (whichever is present).
             new_x_column (str, optional): Label of the destination 'X' column after
-                momentum calibration. Defaults to config["momentum"]["kx_column"].
+                momentum calibration. Defaults to config["dataframe"]["columns"]["kx"].
             new_y_column (str, optional): Label of the destination 'Y' column after
-                momentum calibration. Defaults to config["momentum"]["ky_column"].
+                momentum calibration. Defaults to config["dataframe"]["columns"]["ky"].
             calibration (dict, optional): Dictionary containing calibration parameters.
                 Defaults to 'self.calibration' or config["momentum"]["calibration"].
+            suppress_output (bool, optional): Option to suppress output of diagnostic information.
+                Defaults to False.
             **kwds: Keyword parameters for momentum calibration. Parameters are added
                 to the calibration dictionary.
 
         Returns:
-            Tuple[Union[pd.DataFrame, dask.dataframe.DataFrame], dict]: Dataframe with
+            tuple[pd.DataFrame | dask.dataframe.DataFrame, dict]: Dataframe with
             added columns and momentum calibration metadata dictionary.
         """
         if x_column is None:
@@ -1889,9 +1940,26 @@ def append_k_axis(
             calibration = deepcopy(self.calibration)
 
         if len(kwds) > 0:
-            for key, value in kwds.items():
-                calibration[key] = value
-            calibration["creation_date"] = datetime.now().timestamp()
+            for key in [
+                "rstart",
+                "cstart",
+                "x_center",
+                "y_center",
+                "kx_scale",
+                "ky_scale",
+                "rstep",
+                "cstep",
+            ]:
+                if key in kwds:
+                    calibration[key] = kwds.pop(key)
+            calibration["creation_date"] = datetime.now()
+
+            if len(kwds) > 0:
+                raise TypeError(f"append_k_axis() got unexpected keyword arguments {kwds.keys()}.")
+
+        if "creation_date" in calibration and not suppress_output:
+            datestring = calibration["creation_date"].strftime("%m/%d/%Y, %H:%M:%S")
+            logger.info(f"Using momentum calibration parameters generated on {datestring}")
 
         try:
             (df[new_x_column], df[new_y_column]) = detector_coordinates_2_k_coordinates(
@@ -1927,7 +1995,7 @@ def gather_calibration_metadata(self, calibration: dict = None) -> dict:
         """
         if calibration is None:
             calibration = self.calibration
-        metadata: Dict[Any, Any] = {}
+        metadata: dict[Any, Any] = {}
         try:
             metadata["creation_date"] = calibration["creation_date"]
         except KeyError:
@@ -1966,29 +2034,24 @@ def cm2palette(cmap_name: str) -> list:
 
 def dictmerge(
     main_dict: dict,
-    other_entries: Union[List[dict], Tuple[dict], dict],
+    other_entries: list[dict] | tuple[dict] | dict,
 ) -> dict:
     """Merge a dictionary with other dictionaries.
 
     Args:
         main_dict (dict): Main dictionary.
-        other_entries (Union[List[dict], Tuple[dict], dict]):
+        other_entries (list[dict] | tuple[dict] | dict):
             Other dictionary or composite dictionarized elements.
 
     Returns:
         dict: Merged dictionary.
     """
-    if isinstance(
-        other_entries,
-        (
-            list,
-            tuple,
-        ),
-    ):  # Merge main_dict with a list or tuple of dictionaries
+    # Merge main_dict with a list or tuple of dictionaries
+    if isinstance(other_entries, (list, tuple)):
         for oth in other_entries:
             main_dict = {**main_dict, **oth}
-
-    elif isinstance(other_entries, dict):  # Merge D with a single dictionary
+    # Merge D with a single dictionary
+    elif isinstance(other_entries, dict):
         main_dict = {**main_dict, **other_entries}
 
     return main_dict
@@ -2005,7 +2068,7 @@ def detector_coordinates_2_k_coordinates(
     c_conversion: float,
     r_step: float,
     c_step: float,
-) -> Tuple[float, float]:
+) -> tuple[float, float]:
     """Conversion from detector coordinates (r_det, c_det) to momentum coordinates
     (kr, kc).
 
@@ -2022,7 +2085,7 @@ def detector_coordinates_2_k_coordinates(
         c_step (float): Column stepping factor.
 
     Returns:
-        Tuple[float, float]: Converted momentum space row/column coordinates.
+        tuple[float, float]: Converted momentum space row/column coordinates.
     """
     r_det0 = r_start + r_step * r_center
     c_det0 = c_start + c_step * c_center
@@ -2033,18 +2096,18 @@ def detector_coordinates_2_k_coordinates(
 
 
 def apply_dfield(
-    df: Union[pd.DataFrame, dask.dataframe.DataFrame],
+    df: pd.DataFrame | dask.dataframe.DataFrame,
     dfield: np.ndarray,
     x_column: str,
     y_column: str,
     new_x_column: str,
     new_y_column: str,
-    detector_ranges: List[Tuple],
-) -> Union[pd.DataFrame, dask.dataframe.DataFrame]:
+    detector_ranges: list[tuple],
+) -> pd.DataFrame | dask.dataframe.DataFrame:
     """Application of the inverse displacement-field to the dataframe coordinates.
 
     Args:
-        df (Union[pd.DataFrame, dask.dataframe.DataFrame]): Dataframe to apply the
+        df (pd.DataFrame | dask.dataframe.DataFrame): Dataframe to apply the
             distortion correction to.
         dfield (np.ndarray): The distortion correction field. 3D matrix,
             with column and row distortion fields stacked along the first dimension.
@@ -2052,11 +2115,11 @@ def apply_dfield(
         y_column (str): Label of the 'Y' source column.
         new_x_column (str): Label of the 'X' destination column.
         new_y_column (str): Label of the 'Y' destination column.
-        detector_ranges (List[Tuple]): tuple of pixel ranges of the detector x/y
+        detector_ranges (list[tuple]): tuple of pixel ranges of the detector x/y
             coordinates
 
     Returns:
-        Union[pd.DataFrame, dask.dataframe.DataFrame]: dataframe with added columns
+        pd.DataFrame | dask.dataframe.DataFrame: dataframe with added columns
     """
     x = df[x_column]
     y = df[y_column]
@@ -2074,8 +2137,8 @@ def apply_dfield(
 def generate_inverse_dfield(
     rdeform_field: np.ndarray,
     cdeform_field: np.ndarray,
-    bin_ranges: List[Tuple],
-    detector_ranges: List[Tuple],
+    bin_ranges: list[tuple],
+    detector_ranges: list[tuple],
 ) -> np.ndarray:
     """Generate inverse deformation field using interpolation with griddata.
     Assuming the binning range of the input ``rdeform_field`` and ``cdeform_field``
@@ -2084,8 +2147,8 @@ def generate_inverse_dfield(
     Args:
         rdeform_field (np.ndarray): Row-wise deformation field.
         cdeform_field (np.ndarray): Column-wise deformation field.
-        bin_ranges (List[Tuple]): Detector ranges of the binned coordinates.
-        detector_ranges (List[Tuple]): Ranges of detector coordinates to interpolate to.
+        bin_ranges (list[tuple]): Detector ranges of the binned coordinates.
+        detector_ranges (list[tuple]): Ranges of detector coordinates to interpolate to.
 
     Returns:
         np.ndarray: The calculated inverse deformation field (row/column)
@@ -2146,14 +2209,14 @@ def generate_inverse_dfield(
     return inverse_dfield
 
 
-def load_dfield(file: str) -> Tuple[np.ndarray, np.ndarray]:
+def load_dfield(file: str) -> tuple[np.ndarray, np.ndarray]:
     """Load inverse dfield from file
 
     Args:
         file (str): Path to file containing the inverse dfield
 
     Returns:
-        np.ndarray: the loaded inverse deformation field
+        tuple[np.ndarray, np.ndarray]: the loaded inverse row and column deformation fields
     """
     rdeform_field: np.ndarray = None
     cdeform_field: np.ndarray = None
diff --git a/sed/config/NXmpes_config-HEXTOF.json b/src/sed/config/NXmpes_config-HEXTOF.json
similarity index 100%
rename from sed/config/NXmpes_config-HEXTOF.json
rename to src/sed/config/NXmpes_config-HEXTOF.json
diff --git a/sed/config/NXmpes_config.json b/src/sed/config/NXmpes_config.json
similarity index 100%
rename from sed/config/NXmpes_config.json
rename to src/sed/config/NXmpes_config.json
diff --git a/sed/dataset/datasets.json b/src/sed/config/datasets.json
similarity index 100%
rename from sed/dataset/datasets.json
rename to src/sed/config/datasets.json
diff --git a/sed/config/default.yaml b/src/sed/config/default.yaml
similarity index 73%
rename from sed/config/default.yaml
rename to src/sed/config/default.yaml
index d0f779be..28b9be3b 100644
--- a/sed/config/default.yaml
+++ b/src/sed/config/default.yaml
@@ -3,40 +3,31 @@ core:
   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 time-of-flight data in nanoseconds
-  tof_ns_column: "t_ns"
-  # dataframe column containing analog-to-digital data
-  adc_column: "ADC"
-  # dataframe column containing bias voltage data
-  bias_column: "sampleBias"
-  # 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"
+  # Column settings
+  columns:
+    x: X   # dataframe column containing x coordinates
+    y: Y  # dataframe column containing y coordinates
+    tof: t   # dataframe column containing time-of-flight data
+    tof_ns: t_ns   # dataframe column containing time-of-flight data in nanoseconds
+    corrected_x: Xm   # dataframe column containing corrected x coordinates
+    corrected_y: Ym   # dataframe column containing corrected y coordinates
+    corrected_tof: tm   # dataframe column containing corrected time-of-flight data
+    kx: kx   # dataframe column containing kx coordinates
+    ky: ky   # dataframe column containing ky coordinates
+    energy: energy   # dataframe column containing energy data
+    delay: delay   # dataframe column containing delay data
+    adc: ADC   # dataframe column containing analog-to-digital data
+    bias: sampleBias   # dataframe column containing bias voltage data
+    timestamp: timeStamp  # dataframe column containing timestamp data
+
   # time length of a base time-of-flight bin in s
   tof_binwidth: 4.125e-12
-  # Binning factor of the tof_column-data compared to tof_binwidth (2^(tof_binning-1))
+  # Binning factor of the tof_column-data compared to tof_binwidth
   tof_binning: 1
-  # binning factor used for the adc coordinate (2^(adc_binning-1))
+  # binning factor used for the adc coordinate
   adc_binning: 1
   # list of columns to apply jitter to.
-  jitter_cols: ["@x_column", "@y_column", "@tof_column"]
+  jitter_cols: ["@x", "@y", "@tof"]
   # Jitter amplitude or list of jitter amplitudes. Should equal half the digital step size of each jitter_column
   jitter_amps: 0.5
   # Time stepping in seconds of the successive events in the timed dataframe
@@ -45,7 +36,7 @@ dataframe:
 energy:
   # Number of bins to use for energy calibration traces
   bins: 1000
-  # Bin ranges to use for energy calibration curves (for tof_binning=0)
+  # Bin ranges to use for energy calibration curves (for tof_binning=1)
   ranges: [100000, 150000]
   # Option to normalize energy calibration traces
   normalize: True
@@ -77,7 +68,7 @@ energy:
 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"]
+  axes: ["@x", "@y", "@tof"]
   # Bin numbers used for the respective axes
   bins: [512, 512, 300]
   # bin ranges to use (in unbinned detector coordinates)
@@ -97,8 +88,6 @@ delay:
   # value ranges of the analog-to-digital converter axes used for encoding the delay stage position
   # (in unbinned coordinates)
   adc_range: [1900, 25600]
-  # pump probe time overlap in ps
-  time0: 0
   # if to flip the time axis
   flip_time_axis: False
 
@@ -119,6 +108,6 @@ histogram:
   bins: [80, 80, 80]
   # default axes to use for histogram visualization.
   # Axes names starting with "@" refer to keys in the "dataframe" section
-  axes: ["@x_column", "@y_column", "@tof_column"]
+  axes: ["@x", "@y", "@tof"]
   # default ranges to use for histogram visualization (in unbinned detector coordinates)
   ranges: [[0, 1800], [0, 1800], [0, 150000]]
diff --git a/src/sed/config/flash_example_config.yaml b/src/sed/config/flash_example_config.yaml
new file mode 100644
index 00000000..232a56bb
--- /dev/null
+++ b/src/sed/config/flash_example_config.yaml
@@ -0,0 +1,221 @@
+# This file contains the default configuration for the flash loader.
+core:
+  # defines the loader
+  loader: flash
+  # Since this will run on maxwell most probably, we have a lot of cores at our disposal
+  num_cores: 100
+  # the beamline where experiment took place
+  beamline: pg2
+  # the ID number of the beamtime
+  beamtime_id: 11019101
+  # the year of the beamtime
+  year: 2023
+  # the instrument used
+  instrument: hextof # hextof, wespe, etc
+  # The paths to the raw and parquet data directories. If these are not
+  # provided, the loader will try to find the data based on year beamtimeID etc
+  # paths:
+  # # location of the raw data.
+  #  raw: ""
+  # # location of the intermediate parquet files.
+  #  processed: ""
+  # The prefixes of the stream names for different DAQ systems for parsing filenames
+  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/"
+
+binning:
+  # Histogram computation mode to use.
+  hist_mode: "numba"
+  # Mode for histogram 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"
+
+dataframe:
+  daq: fl1user3                                   # DAQ system name to resolve filenames/paths
+  ubid_offset: 5                                  # Offset correction to the pulseId
+  forward_fill_iterations: 2                      # Number of iterations to fill the pulseId forward
+  split_sector_id_from_dld_time: True             # Remove reserved bits for dldSectorID from dldTimeSteps column
+  sector_id_reserved_bits: 3                      # Bits reserved for dldSectorID in the dldTimeSteps column
+  sector_delays: [0., 0., 0., 0., 0., 0., 0., 0.] # Sector delays
+
+  # Time and binning settings
+  tof_binwidth: 2.0576131995767355E-11            # Base time-of-flight bin width in seconds
+  tof_binning: 8                                  # Binning parameter for time-of-flight data
+
+  # Columns used for jitter correction
+  jitter_cols: [dldPosX, dldPosY, dldTimeSteps]
+
+  # Column settings
+  columns:
+    x: dldPosX
+    corrected_x: X
+    kx: kx
+    y: dldPosY
+    corrected_y: Y
+    ky: ky
+    tof: dldTimeSteps
+    tof_ns: dldTime
+    corrected_tof: tm
+    timestamp: timeStamp
+    auxiliary: dldAux
+    sector_id: dldSectorID
+    delay: delayStage
+    corrected_delay: pumpProbeTime
+
+  # These are the units of the columns
+  units:
+    dldPosX: 'step'
+    dldPosY: 'step'
+    dldTimeSteps: 'step'
+    tof_voltage: 'V'
+    extractorVoltage: 'V'
+    extractorCurrent: 'A'
+    cryoTemperature: 'K'
+    sampleTemperature: 'K'
+    dldTime: 'ns'
+    delay: 'ps'
+    delayStage: 'ps'
+    timeStamp: 's'
+    energy: 'eV'
+    E: 'eV'
+    kx: '1/A'
+    ky: '1/A'
+
+  # The channels to load from the raw data. The channels have the following structure:
+  # channels have the following structure:
+  # <channelAlias>:
+  #   format: per_pulse/per_electron/per_train
+  #   index_key: the hdf5 index key
+  #   dataset_key: the hdf5 dataset key
+  #   slice: int to slice a multidimensional data along axis=1. If not defined, there is no slicing
+  #   dtype: the datatype of the data
+  #   subChannels: further aliases for if the data is multidimensional and needs to be split in different cols
+  #                used currently for the auxiliary channel
+  #      <subChannelAlias>:
+  #        slice: int to slice a multidimensional data along axis=1. Must be defined
+  #        dtype: the datatype of the data
+
+  channels:
+    # The timestamp
+    timeStamp:
+      format: per_train
+      index_key: "/uncategorised/FLASH.DIAG/TIMINGINFO/TIME1.BUNCH_FIRST_INDEX.1/index"
+      dataset_key: "/uncategorised/FLASH.DIAG/TIMINGINFO/TIME1.BUNCH_FIRST_INDEX.1/time"
+
+    # pulse ID is a necessary channel for using the loader.
+    pulseId:
+      format: per_electron
+      index_key: "/uncategorised/FLASH.EXP/HEXTOF.DAQ/DLD1/index"
+      dataset_key: "/uncategorised/FLASH.EXP/HEXTOF.DAQ/DLD1/value"
+      slice: 2
+      dtype: uint16
+
+    # detector x position
+    dldPosX:
+      format: per_electron
+      index_key: "/uncategorised/FLASH.EXP/HEXTOF.DAQ/DLD1/index"
+      dataset_key: "/uncategorised/FLASH.EXP/HEXTOF.DAQ/DLD1/value"
+      slice: 1
+      dtype: uint16
+
+    # detector y position
+    dldPosY:
+      format: per_electron
+      index_key: "/uncategorised/FLASH.EXP/HEXTOF.DAQ/DLD1/index"
+      dataset_key: "/uncategorised/FLASH.EXP/HEXTOF.DAQ/DLD1/value"
+      slice: 0
+      dtype: uint16
+
+    # Detector time-of-flight channel
+    # if split_sector_id_from_dld_time is set to True, This this will generate
+    # also the dldSectorID channel
+    dldTimeSteps:
+      format: per_electron
+      index_key: "/uncategorised/FLASH.EXP/HEXTOF.DAQ/DLD1/index"
+      dataset_key: "/uncategorised/FLASH.EXP/HEXTOF.DAQ/DLD1/value"
+      slice: 3
+      dtype: uint32
+
+    # The auxiliary channel has a special structure where the group further contains
+    # a multidimensional structure so further aliases are defined below
+    dldAux:
+      format: per_train
+      index_key: "/uncategorised/FLASH.EXP/HEXTOF.DAQ/DLD1/index"
+      dataset_key: "/uncategorised/FLASH.EXP/HEXTOF.DAQ/DLD1/value"
+      slice: 4
+      sub_channels:
+        sampleBias:
+          slice: 0
+          dtype: float32
+        tofVoltage:
+          slice: 1
+          dtype: float64
+        extractorVoltage:
+          slice: 2
+        extractorCurrent:
+          slice: 3
+        cryoTemperature:
+          slice: 4
+        sampleTemperature:
+          slice: 5
+        dldTimeBinSize:
+          slice: 15
+
+    # ADC containing the pulser sign (1: value approx. 35000, 0: 33000)
+    pulserSignAdc:
+      format: per_pulse
+      index_key: "/FL1/Experiment/PG/SIS8300 100MHz ADC/CH6/TD/index"
+      dataset_key: "/FL1/Experiment/PG/SIS8300 100MHz ADC/CH6/TD/value"
+
+    # the energy of the monochromatized beam. This is a quasi-static value.
+    # there is a better channel which still needs implementation.
+    monochromatorPhotonEnergy:
+      format: per_train
+      index_key: "/FL1/Beamlines/PG/Monochromator/monochromator photon energy/index"
+      dataset_key: "/FL1/Beamlines/PG/Monochromator/monochromator photon energy/value"
+
+    # The GMDs can not be read yet...
+    gmdBda:
+      format: per_train
+      index_key: "/FL1/Photon Diagnostic/GMD/Average energy/energy BDA/index"
+      dataset_key: "/FL1/Photon Diagnostic/GMD/Average energy/energy BDA/value"
+
+    # Beam Arrival Monitor, vital for pump-probe experiments as it can compensate sase
+    # timing fluctuations.
+    # Here we use the DBC2 BAM as the "normal" one is broken.
+    bam:
+        format: per_pulse
+        index_key: "/uncategorised/FLASH.SDIAG/BAM.DAQ/FL0.DBC2.ARRIVAL_TIME.ABSOLUTE.SA1.COMP/index"
+        dataset_key: "/uncategorised/FLASH.SDIAG/BAM.DAQ/FL0.DBC2.ARRIVAL_TIME.ABSOLUTE.SA1.COMP/value"
+
+    # The delay Stage position, encoding the pump-probe delay
+    delayStage:
+      format: per_train
+      index_key: "/zraw/FLASH.SYNC/LASER.LOCK.EXP/F1.PG.OSC/FMC0.MD22.1.ENCODER_POSITION.RD/dGroup/index"
+      dataset_key: "/zraw/FLASH.SYNC/LASER.LOCK.EXP/F1.PG.OSC/FMC0.MD22.1.ENCODER_POSITION.RD/dGroup/value"
+
+# metadata collection from scicat
+# metadata:
+#   scicat_url: <URL>
+#   scicat_token: <TOKEN>
+
+# The nexus collection routine shall be finalized soon for both instruments
+nexus:
+  reader: "mpes"
+  definition: "NXmpes"
+  input_files: ["../src/sed/config/NXmpes_config-HEXTOF.json"]
diff --git a/sed/config/mpes_example_config.yaml b/src/sed/config/mpes_example_config.yaml
similarity index 81%
rename from sed/config/mpes_example_config.yaml
rename to src/sed/config/mpes_example_config.yaml
index 37a2b5ae..43e7db5f 100644
--- a/sed/config/mpes_example_config.yaml
+++ b/src/sed/config/mpes_example_config.yaml
@@ -1,66 +1,46 @@
 core:
   # The loader to use. The mpes loader allows for loading hdf5 files from the METIS momentum microscope.
   loader: mpes
+  # Number of parallel threads to use for parallelized jobs (e.g. binning, data conversion, copy, ...)
+  num_cores: 20
   # 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 keywords 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
+  # copy_tool:
+    # # path to the root of the source data directory
+    # source: "/path/to/data/"
+    # # path to the root or the local data storage
+    # dest: "/path/to/localDataStore/"
+    # # group id to set for copied files and folders
+    # gid: 1000
 
 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 occurring at every millisecond (used to calculate timestamps)
-  ms_markers_group: "msMarkers"
+  ms_markers_key: "msMarkers"
   # hdf5 attribute containing the timestamp of the first event in a file
   first_event_time_stamp_key: "FirstEventTimeStamp"
   # Time stepping in seconds of the successive events in the timed dataframe
   timed_dataframe_unit_time: 0.001
   # 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"
+  columns:
+    x: X # dataframe column containing x coordinates
+    y: Y # dataframe column containing y coordinates
+    tof: t # dataframe column containing time-of-flight data
+    adc: ADC # dataframe column containing analog-to-digital data
+    bias: sampleBias # dataframe column containing bias voltage data
+    corrected_x: Xm # dataframe column containing corrected x coordinates
+    corrected_y: Ym # dataframe column containing corrected y coordinates
+    corrected_tof: tm # dataframe column containing corrected time-of-flight data
+    kx: kx # dataframe column containing kx coordinates
+    ky: ky # dataframe column containing ky coordinates
+    energy: energy # dataframe column containing energy data
+    delay: delay # dataframe column containing delay data
+    timestamp: timeStamps  # dataframe column containing timestamp data
   # 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
+  # Binning factor of the tof_column-data compared to tof_binwidth
+  tof_binning: 4
+  # binning factor used for the adc coordinate
+  adc_binning: 4
   # Default units for dataframe entries
   units:
     X: 'step'
@@ -79,11 +59,34 @@ dataframe:
     kx: '1/A'
     ky: '1/A'
 
+  # dataframe channels and group names to read from the h5 files
+  channels:
+    # The X-channel
+    X:
+      format: per_electron
+      dataset_key: "Stream_0"
+    # The Y-channel
+    Y:
+      format: per_electron
+      dataset_key: "Stream_1"
+    # The tof-channel
+    t:
+      format: per_electron
+      dataset_key: "Stream_2"
+    # The ADC-channel
+    ADC:
+      format: per_electron
+      dataset_key: "Stream_4"
+    # The sample Bias-channel
+    sampleBias:
+      format: per_file
+      dataset_key: "KTOF:Lens:Sample:V"
+
 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]
+  # Bin ranges to use for energy calibration curves (for tof_binning=1)
+  ranges: [256000, 276000]
   # hdf5 path to attribute storing bias information for a given file
   bias_key: "@KTOF:Lens:Sample:V"
   # Option to normalize energy calibration traces
@@ -102,7 +105,7 @@ energy:
   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_fermi: 264500
   # TOF range to visualize for the correction tool around tof_fermi
   tof_width: [-600, 1000]
   # x-integration range for the correction tool around the center pixel
@@ -138,11 +141,11 @@ energy:
 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"]
+  axes: ["@x", "@y", "@tof"]
   # 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]]
+  ranges: [[-256, 1792], [-256, 1792], [264000, 272000]]
   # 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
@@ -199,8 +202,6 @@ binning:
   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
@@ -211,9 +212,9 @@ histogram:
   bins: [80, 80, 80, 80]
   # default axes to use for histogram visualization.
   # Axes names starting with "@" refer to keys in the "dataframe" section
-  axes: ["@x_column", "@y_column", "@tof_column", "@adc_column"]
+  axes: ["@x", "@y", "@tof", "@adc"]
   # default ranges to use for histogram visualization (in unbinned detector coordinates)
-  ranges: [[0, 1800], [0, 1800], [128000, 138000], [0, 32000]]
+  ranges: [[0, 1800], [0, 1800], [256000, 276000], [0, 32000]]
 
 metadata:
   # URL of the epics archiver request engine
@@ -309,4 +310,4 @@ nexus:
   definition: "NXmpes"
   # List containing 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"]
+  input_files: ["../src/sed/config/NXmpes_config.json"]
diff --git a/sed/config/sxp_example_config.yaml b/src/sed/config/sxp_example_config.yaml
similarity index 72%
rename from sed/config/sxp_example_config.yaml
rename to src/sed/config/sxp_example_config.yaml
index 3c7e0f90..832a45c1 100644
--- a/sed/config/sxp_example_config.yaml
+++ b/src/sed/config/sxp_example_config.yaml
@@ -1,16 +1,33 @@
 core:
   loader: sxp
+  # Since this will run on maxwell most probably, we have a lot of cores at our disposal
+  num_cores: 100
   beamtime_id: p005639
   year: 202302
   beamline: sxp
   instrument: sxp
+  stream_name_prefixes:
+    DA03: "RAW-R"
+  stream_name_postfixes:
+    DA03: "-DA03-"
+  beamtime_dir:
+    sxp: "/gpfs/exfel/exp/SXP/"
   paths:
-    data_raw_dir: "/path/to/data"
+    raw: "/path/to/data"
     # change this to a local directory where you want to store the parquet files
-    data_parquet_dir: "/path/to/parquet"
+    processed: "/path/to/parquet"
 
 binning:
-  num_cores: 10
+  # Histogram computation mode to use.
+  hist_mode: "numba"
+  # Mode for histogram 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"
 
 dataframe:
   ubid_offset: 0
@@ -18,21 +35,27 @@ dataframe:
   forward_fill_iterations: 2
   num_trains: 10
   # num_pulses: 400 # only needed for data from new DAQ
-  x_column: dldPosX
-  corrected_x_column: "X"
-  kx_column: "kx"
-  y_column: dldPosY
-  corrected_y_column: "Y"
-  ky_column: "ky"
-  tof_column: dldTimeSteps
-  tof_ns_column: dldTime
-  corrected_tof_column: "tm"
-  bias_column: "sampleBias"
-  delay_column: "delayStage"
   tof_binwidth: 6.875E-12 # in seconds
-  tof_binning: 0
+  tof_binning: 1
   jitter_cols: ["dldPosX", "dldPosY", "dldTimeSteps"]
 
+  # Column settings
+  columns:
+    x: dldPosX
+    corrected_x: X
+    kx: kx
+    y: dldPosY
+    corrected_y: Y
+    ky: ky
+    tof: dldTimeSteps
+    tof_ns: dldTime
+    corrected_tof: tm
+    timestamp: timeStamp
+    auxiliary: dldAux
+    sector_id: dldSectorID
+    delay: delayStage
+    corrected_delay: pumpProbeTime
+
   units:
     dldPosX: 'step'
     dldPosY: 'step'
@@ -86,25 +109,17 @@ dataframe:
       format: per_train
       dataset_key: "/CONTROL/SCS_ILH_LAS/MDL/OPTICALDELAY_PP800/actualPosition/value"
       index_key: "/INDEX/trainId"
-#     test:
-#       daq: DA02 # change DAQ for a channel
-#       format: per_pulse
-#       dataset_key: "/INSTRUMENT/SA3_XTD10_XGM/XGM/DOOCS:output/data/intensitySa3TD"
-#       index_key: "/INSTRUMENT/SA3_XTD10_XGM/XGM/DOOCS:output/data/trainId"
-
-  stream_name_prefixes:
-    DA03: "RAW-R"
-  stream_name_postfixes:
-    DA03: "-DA03-"
-
-  beamtime_dir:
-    sxp: "/gpfs/exfel/exp/SXP/"
+    # test:
+    #   daq: DA02 # change DAQ for a channel
+    #   format: per_pulse
+    #   dataset_key: "/INSTRUMENT/SA3_XTD10_XGM/XGM/DOOCS:output/data/intensitySa3TD"
+    #   index_key: "/INSTRUMENT/SA3_XTD10_XGM/XGM/DOOCS:output/data/trainId"
 
 histogram:
   # number of bins used for histogram visualization
   bins: [80, 80, 80, 80]
   # default axes to use for histogram visualization.
   # Axes names starting with "@" refer to keys in the "dataframe" section
-  axes: ["@x_column", "@y_column", "@tof_column", "@delay_column"]
+  axes: ["@x", "@y", "@tof", "@delay"]
   # default ranges to use for histogram visualization (in unbinned detector coordinates)
   ranges: [[0, 4000], [0, 4000], [1000, 28000], [-1000, 1000]]
diff --git a/sed/core/__init__.py b/src/sed/core/__init__.py
similarity index 100%
rename from sed/core/__init__.py
rename to src/sed/core/__init__.py
diff --git a/sed/core/config.py b/src/sed/core/config.py
similarity index 55%
rename from sed/core/config.py
rename to src/sed/core/config.py
index a16fc847..d9c7b551 100644
--- a/sed/core/config.py
+++ b/src/sed/core/config.py
@@ -1,31 +1,43 @@
 """This module contains a config library for loading yaml/json files into dicts
 """
+from __future__ import annotations
+
 import copy
 import json
 import os
 import platform
 from importlib.util import find_spec
 from pathlib import Path
-from typing import Union
 
 import yaml
 from platformdirs import user_config_path
+from pydantic import ValidationError
+
+from sed.core.config_model import ConfigModel
+from sed.core.logging import setup_logging
 
 package_dir = os.path.dirname(find_spec("sed").origin)
 
 USER_CONFIG_PATH = user_config_path(appname="sed", appauthor="OpenCOMPES", ensure_exists=True)
+SYSTEM_CONFIG_PATH = (
+    Path(os.environ["ALLUSERSPROFILE"]).joinpath("sed")
+    if platform.system() == "Windows"
+    else Path("/etc/").joinpath("sed")
+)
+ENV_DIR = Path(".env")
+
+# Configure logging
+logger = setup_logging("config")
 
 
 def parse_config(
-    config: Union[dict, str] = None,
-    folder_config: Union[dict, str] = None,
-    user_config: Union[dict, str] = None,
-    system_config: Union[dict, str] = None,
-    default_config: Union[
-        dict,
-        str,
-    ] = f"{package_dir}/config/default.yaml",
+    config: dict | str = None,
+    folder_config: dict | str = None,
+    user_config: dict | str = None,
+    system_config: dict | str = None,
+    default_config: (dict | str) = f"{package_dir}/config/default.yaml",
     verbose: bool = True,
+    verify_config: bool = True,
 ) -> dict:
     """Load the config dictionary from a file, or pass the provided config dictionary.
     The content of the loaded config dictionary is then completed from a set of pre-configured
@@ -34,30 +46,32 @@ def parse_config(
     can be also passed as optional arguments (file path strings or dictionaries).
 
     Args:
-        config (Union[dict, str], optional): config dictionary or file path.
+        config (dict | str, optional): config dictionary or file path.
                 Files can be *json* or *yaml*. Defaults to None.
-        folder_config (Union[ dict, str, ], optional): working-folder-based config dictionary
+        folder_config (dict | str, optional): working-folder-based config dictionary
             or file path. The loaded dictionary is completed with the folder-based values,
             taking preference over user, system and default values. Defaults to the file
             "sed_config.yaml" in the current working directory.
-        user_config (Union[ dict, str, ], optional): user-based config dictionary
+        user_config (dict | str, optional): user-based config dictionary
             or file path. The loaded dictionary is completed with the user-based values,
             taking preference over system and default values.
-            Defaults to the file ".sed/config.yaml" in the current user's home directory.
-        system_config (Union[ dict, str, ], optional): system-wide config dictionary
+            Defaults to the file ".config/sed/config_v1.yaml" in the current user's home directory.
+        system_config (dict | str, optional): system-wide config dictionary
             or file path. The loaded dictionary is completed with the system-wide values,
-            taking preference over default values. Defaults to the file "/etc/sed/config.yaml"
-            on linux, and "%ALLUSERSPROFILE%/sed/config.yaml" on windows.
-        default_config (Union[ dict, str, ], optional): default config dictionary
+            taking preference over default values. Defaults to the file "/etc/sed/config_v1.yaml"
+            on linux, and "%ALLUSERSPROFILE%/sed/config_v1.yaml" on windows.
+        default_config (dict | str, optional): default config dictionary
             or file path. The loaded dictionary is completed with the default values.
             Defaults to *package_dir*/config/default.yaml".
         verbose (bool, optional): Option to report loaded config files. Defaults to True.
+        verify_config (bool, optional): Option to verify config file. Defaults to True.
     Raises:
         TypeError: Raised if the provided file is neither *json* nor *yaml*.
         FileNotFoundError: Raised if the provided file is not found.
+        ValueError: Raised if there is a validation error in the config file.
 
     Returns:
-        dict: Loaded and possibly completed config dictionary.
+        dict: Loaded and completed config dict, possibly verified by pydantic config model.
     """
     if config is None:
         config = {}
@@ -67,7 +81,7 @@ def parse_config(
     else:
         config_dict = load_config(config)
         if verbose:
-            print(f"Configuration loaded from: [{str(Path(config).resolve())}]")
+            logger.info(f"Configuration loaded from: [{str(Path(config).resolve())}]")
 
     folder_dict: dict = None
     if isinstance(folder_config, dict):
@@ -78,45 +92,36 @@ def parse_config(
         if Path(folder_config).exists():
             folder_dict = load_config(folder_config)
             if verbose:
-                print(f"Folder config loaded from: [{str(Path(folder_config).resolve())}]")
+                logger.info(f"Folder config loaded from: [{str(Path(folder_config).resolve())}]")
 
     user_dict: dict = None
     if isinstance(user_config, dict):
         user_dict = copy.deepcopy(user_config)
     else:
         if user_config is None:
-            user_config = str(
-                Path.home().joinpath(".sed").joinpath("config.yaml"),
-            )
+            user_config = str(USER_CONFIG_PATH.joinpath("config_v1.yaml"))
         if Path(user_config).exists():
             user_dict = load_config(user_config)
             if verbose:
-                print(f"User config loaded from: [{str(Path(user_config).resolve())}]")
+                logger.info(f"User config loaded from: [{str(Path(user_config).resolve())}]")
 
     system_dict: dict = None
     if isinstance(system_config, dict):
         system_dict = copy.deepcopy(system_config)
     else:
         if system_config is None:
-            if platform.system() in ["Linux", "Darwin"]:
-                system_config = str(
-                    Path("/etc/").joinpath("sed").joinpath("config.yaml"),
-                )
-            elif platform.system() == "Windows":
-                system_config = str(
-                    Path(os.environ["ALLUSERSPROFILE"]).joinpath("sed").joinpath("config.yaml"),
-                )
+            system_config = str(SYSTEM_CONFIG_PATH.joinpath("config_v1.yaml"))
         if Path(system_config).exists():
             system_dict = load_config(system_config)
             if verbose:
-                print(f"System config loaded from: [{str(Path(system_config).resolve())}]")
+                logger.info(f"System config loaded from: [{str(Path(system_config).resolve())}]")
 
     if isinstance(default_config, dict):
         default_dict = copy.deepcopy(default_config)
     else:
         default_dict = load_config(default_config)
         if verbose:
-            print(f"Default config loaded from: [{str(Path(default_config).resolve())}]")
+            logger.info(f"Default config loaded from: [{str(Path(default_config).resolve())}]")
 
     if folder_dict is not None:
         config_dict = complete_dictionary(
@@ -138,7 +143,21 @@ def parse_config(
         base_dictionary=default_dict,
     )
 
-    return config_dict
+    if not verify_config:
+        return config_dict
+
+    try:
+        # Run the config through the ConfigModel to ensure it is valid
+        config_model = ConfigModel(**config_dict)
+        return config_model.model_dump(exclude_unset=True, exclude_none=True)
+    except ValidationError as e:
+        error_msg = (
+            "Invalid configuration file detected. The following validation errors were found:\n"
+        )
+        for error in e.errors():
+            error_msg += f"\n- {' -> '.join(str(loc) for loc in error['loc'])}: {error['msg']}"
+        logger.error(error_msg)
+        raise ValueError(error_msg) from e
 
 
 def load_config(config_path: str) -> dict:
@@ -156,9 +175,9 @@ def load_config(config_path: str) -> dict:
     """
     config_file = Path(config_path)
     if not config_file.is_file():
-        raise FileNotFoundError(
-            f"could not find the configuration file: {config_file}",
-        )
+        error_message = f"could not find the configuration file: {config_file}"
+        logger.error(error_message)
+        raise FileNotFoundError(error_message)
 
     if config_file.suffix == ".json":
         with open(config_file, encoding="utf-8") as stream:
@@ -167,7 +186,9 @@ def load_config(config_path: str) -> dict:
         with open(config_file, encoding="utf-8") as stream:
             config_dict = yaml.safe_load(stream)
     else:
-        raise TypeError("config file must be of type json or yaml!")
+        error_message = "config file must be of type json or yaml!"
+        logger.error(error_message)
+        raise TypeError(error_message)
 
     return config_dict
 
@@ -230,3 +251,85 @@ def complete_dictionary(dictionary: dict, base_dictionary: dict) -> dict:
                     dictionary[k] = v
 
     return dictionary
+
+
+def _parse_env_file(file_path: Path) -> dict:
+    """Helper function to parse a .env file into a dictionary.
+
+    Args:
+        file_path (Path): Path to the .env file
+
+    Returns:
+        dict: Dictionary of environment variables from the file
+    """
+    env_content = {}
+    if file_path.exists():
+        with open(file_path) as f:
+            for line in f:
+                line = line.strip()
+                if line and "=" in line:
+                    key, val = line.split("=", 1)
+                    env_content[key.strip()] = val.strip()
+    return env_content
+
+
+def read_env_var(var_name: str) -> str | None:
+    """Read an environment variable from multiple locations in order:
+    1. OS environment variables
+    2. .env file in current directory
+    3. .env file in user config directory
+    4. .env file in system config directory
+
+    Args:
+        var_name (str): Name of the environment variable to read
+
+    Returns:
+        str | None: Value of the environment variable or None if not found
+    """
+    # 1. check OS environment variables
+    value = os.getenv(var_name)
+    if value is not None:
+        logger.debug(f"Found {var_name} in OS environment variables")
+        return value
+
+    # 2. check .env in current directory
+    local_vars = _parse_env_file(ENV_DIR)
+    if var_name in local_vars:
+        logger.debug(f"Found {var_name} in ./.env file")
+        return local_vars[var_name]
+
+    # 3. check .env in user config directory
+    user_vars = _parse_env_file(USER_CONFIG_PATH / ".env")
+    if var_name in user_vars:
+        logger.debug(f"Found {var_name} in user config .env file")
+        return user_vars[var_name]
+
+    # 4. check .env in system config directory
+    system_vars = _parse_env_file(SYSTEM_CONFIG_PATH / ".env")
+    if var_name in system_vars:
+        logger.debug(f"Found {var_name} in system config .env file")
+        return system_vars[var_name]
+
+    logger.debug(f"Environment variable {var_name} not found in any location")
+    return None
+
+
+def save_env_var(var_name: str, value: str) -> None:
+    """Save an environment variable to the .env file in the user config directory.
+    If the file exists, preserves other variables. If not, creates a new file.
+
+    Args:
+        var_name (str): Name of the environment variable to save
+        value (str): Value to save for the environment variable
+    """
+    env_path = USER_CONFIG_PATH / ".env"
+    env_content = _parse_env_file(env_path)
+
+    # Update or add new variable
+    env_content[var_name] = value
+
+    # Write all variables back to file
+    with open(env_path, "w") as f:
+        for key, val in env_content.items():
+            f.write(f"{key}={val}\n")
+    logger.debug(f"Environment variable {var_name} saved to .env file")
diff --git a/src/sed/core/config_model.py b/src/sed/core/config_model.py
new file mode 100644
index 00000000..6379b639
--- /dev/null
+++ b/src/sed/core/config_model.py
@@ -0,0 +1,354 @@
+"""Pydantic model to validate the config for SED package."""
+import grp
+from collections.abc import Sequence
+from datetime import datetime
+from typing import Literal
+from typing import Optional
+from typing import Union
+
+from pydantic import BaseModel
+from pydantic import ConfigDict
+from pydantic import DirectoryPath
+from pydantic import field_validator
+from pydantic import FilePath
+from pydantic import HttpUrl
+from pydantic import NewPath
+from pydantic import PositiveInt
+
+from sed.loader.loader_interface import get_names_of_all_loaders
+
+## Best to not use futures annotations with pydantic models
+## https://github.com/astral-sh/ruff/issues/5434
+
+
+class PathsModel(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    raw: DirectoryPath
+    processed: Optional[Union[DirectoryPath, NewPath]] = None
+
+
+class CopyToolModel(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    source: DirectoryPath
+    dest: DirectoryPath
+    safety_margin: Optional[float] = None
+    gid: Optional[int] = None
+    scheduler: Optional[str] = None
+
+    @field_validator("gid")
+    @classmethod
+    def validate_gid(cls, v: int) -> int:
+        """Checks if the gid is valid on the system"""
+        try:
+            grp.getgrgid(v)
+        except KeyError:
+            raise ValueError(f"Invalid value {v} for gid. Group not found.")
+        return v
+
+
+class CoreModel(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    loader: str
+    verbose: Optional[bool] = None
+    paths: Optional[PathsModel] = None
+    num_cores: Optional[PositiveInt] = None
+    year: Optional[int] = None
+    beamtime_id: Optional[Union[int, str]] = None
+    instrument: Optional[str] = None
+    beamline: Optional[str] = None
+    copy_tool: Optional[CopyToolModel] = None
+    stream_name_prefixes: Optional[dict] = None
+    stream_name_postfixes: Optional[dict] = None
+    beamtime_dir: Optional[dict] = None
+
+    @field_validator("loader")
+    @classmethod
+    def validate_loader(cls, v: str) -> str:
+        """Checks if the loader is one of the valid ones"""
+        names = get_names_of_all_loaders()
+        if v not in names:
+            raise ValueError(f"Invalid loader {v}. Available loaders are: {names}")
+        return v
+
+
+class ColumnsModel(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    x: str
+    y: str
+    tof: str
+    tof_ns: str
+    kx: str
+    ky: str
+    energy: str
+    delay: str
+    adc: str
+    bias: str
+    timestamp: str
+    corrected_x: str
+    corrected_y: str
+    corrected_tof: str
+    corrected_delay: Optional[str] = None
+    sector_id: Optional[str] = None
+    auxiliary: Optional[str] = None
+
+
+class ChannelModel(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    format: Literal["per_train", "per_electron", "per_pulse", "per_file"]
+    dataset_key: str
+    index_key: Optional[str] = None
+    slice: Optional[int] = None
+    dtype: Optional[str] = None
+    max_hits: Optional[int] = None
+    scale: Optional[float] = None
+    daq: Optional[str] = None
+
+    class subChannel(BaseModel):
+        model_config = ConfigDict(extra="forbid")
+
+        slice: int
+        dtype: Optional[str] = None
+
+    sub_channels: Optional[dict[str, subChannel]] = None
+
+
+class DataframeModel(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    columns: ColumnsModel
+    units: Optional[dict[str, str]] = None
+    channels: Optional[dict[str, ChannelModel]] = None
+    # other settings
+    tof_binwidth: float
+    tof_binning: int
+    adc_binning: int
+    jitter_cols: Sequence[str]
+    jitter_amps: Union[float, Sequence[float]]
+    timed_dataframe_unit_time: float
+    # mpes specific settings
+    first_event_time_stamp_key: Optional[str] = None
+    ms_markers_key: Optional[str] = None
+    # flash specific settings
+    forward_fill_iterations: Optional[int] = None
+    ubid_offset: Optional[int] = None
+    split_sector_id_from_dld_time: Optional[bool] = None
+    sector_id_reserved_bits: Optional[int] = None
+    sector_delays: Optional[Sequence[float]] = None
+    daq: Optional[str] = None
+    # SXP specific settings
+    num_trains: Optional[PositiveInt] = None
+    num_pulses: Optional[PositiveInt] = None
+
+
+class BinningModel(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    hist_mode: Literal["numpy", "numba"]
+    mode: Literal["fast", "lean", "legacy"]
+    pbar: bool
+    threads_per_worker: PositiveInt
+    threadpool_API: Literal["blas", "openmp"]
+
+
+class HistogramModel(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    bins: Sequence[PositiveInt]
+    axes: Sequence[str]
+    ranges: Sequence[tuple[float, float]]
+
+
+class EnergyModel(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    bins: PositiveInt
+    ranges: tuple[int, int]
+    normalize: bool
+    normalize_span: int
+    normalize_order: int
+    fastdtw_radius: int
+    peak_window: int
+    calibration_method: Literal["lmfit", "lstsq", "lsq"]
+    energy_scale: Literal["binding", "kinetic"]
+    tof_fermi: int
+    tof_width: tuple[int, int]
+    x_width: tuple[int, int]
+    y_width: tuple[int, int]
+    color_clip: int
+    bias_key: Optional[str] = None
+
+    class EnergyCalibrationModel(BaseModel):
+        model_config = ConfigDict(extra="forbid")
+
+        creation_date: Optional[datetime] = None
+        d: Optional[float] = None
+        t0: Optional[float] = None
+        E0: Optional[float] = None
+        coeffs: Optional[Sequence[float]] = None
+        offset: Optional[float] = None
+        energy_scale: Literal["binding", "kinetic"]
+
+    calibration: Optional[EnergyCalibrationModel] = None
+
+    class EnergyOffsets(BaseModel):
+        model_config = ConfigDict(extra="forbid")
+
+        creation_date: Optional[datetime] = None
+        constant: Optional[float] = None
+
+        class OffsetColumn(BaseModel):
+            weight: float
+            preserve_mean: bool
+            reduction: Optional[str] = None
+
+        columns: Optional[dict[str, OffsetColumn]] = None
+
+    offsets: Optional[EnergyOffsets] = None
+
+    class EnergyCorrectionModel(BaseModel):
+        model_config = ConfigDict(extra="forbid")
+
+        creation_date: Optional[datetime] = None
+        correction_type: Literal["Gaussian", "Lorentzian", "spherical", "Lorentzian_asymmetric"]
+        amplitude: float
+        center: tuple[float, float]
+        gamma: Optional[float] = None
+        sigma: Optional[float] = None
+        diameter: Optional[float] = None
+        sigma2: Optional[float] = None
+        amplitude2: Optional[float] = None
+
+    correction: Optional[EnergyCorrectionModel] = None
+
+
+class MomentumModel(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    axes: Sequence[str]
+    bins: Sequence[PositiveInt]
+    ranges: Sequence[tuple[int, int]]
+    detector_ranges: Sequence[tuple[int, int]]
+    center_pixel: tuple[int, int]
+    sigma: int
+    fwhm: int
+    sigma_radius: int
+
+    class MomentumCalibrationModel(BaseModel):
+        model_config = ConfigDict(extra="forbid")
+
+        creation_date: Optional[datetime] = None
+        kx_scale: float
+        ky_scale: float
+        x_center: float
+        y_center: float
+        rstart: float
+        cstart: float
+        rstep: float
+        cstep: float
+
+    calibration: Optional[MomentumCalibrationModel] = None
+
+    class MomentumCorrectionModel(BaseModel):
+        model_config = ConfigDict(extra="forbid")
+
+        creation_date: Optional[datetime] = None
+        feature_points: Sequence[tuple[float, float]]
+        rotation_symmetry: PositiveInt
+        include_center: bool
+        use_center: bool
+        ascale: Optional[Sequence[float]] = None
+        center_point: Optional[tuple[float, float]] = None
+        outer_points: Optional[Sequence[tuple[float, float]]] = None
+
+    correction: Optional[MomentumCorrectionModel] = None
+
+    class MomentumTransformationsModel(BaseModel):
+        model_config = ConfigDict(extra="forbid")
+
+        creation_date: Optional[datetime] = None
+        scale: Optional[float] = None
+        angle: Optional[float] = None
+        xtrans: Optional[float] = None
+        ytrans: Optional[float] = None
+
+    transformations: Optional[MomentumTransformationsModel] = None
+
+
+class DelayModel(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    adc_range: tuple[int, int]
+    flip_time_axis: bool
+    # Group keys in the datafile
+    p1_key: Optional[str] = None
+    p2_key: Optional[str] = None
+    t0_key: Optional[str] = None
+
+    class DelayCalibration(BaseModel):
+        model_config = ConfigDict(extra="forbid")
+
+        creation_date: Optional[datetime] = None
+        adc_range: Optional[tuple[int, int]] = None
+        delay_range: Optional[tuple[float, float]] = None
+        time0: Optional[float] = None
+        delay_range_mm: Optional[tuple[float, float]] = None
+        datafile: Optional[FilePath]  # .h5 extension in filepath
+
+    calibration: Optional[DelayCalibration] = None
+
+    class DelayOffsets(BaseModel):
+        model_config = ConfigDict(extra="forbid")
+
+        creation_date: Optional[datetime] = None
+        constant: Optional[float] = None
+        flip_delay_axis: Optional[bool] = False
+
+        class OffsetColumn(BaseModel):
+            weight: float
+            preserve_mean: bool
+            reduction: Optional[str] = None
+
+        columns: Optional[dict[str, OffsetColumn]] = None
+
+    offsets: Optional[DelayOffsets] = None
+
+
+class MetadataModel(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    archiver_url: Optional[HttpUrl] = None
+    epics_pvs: Optional[Sequence[str]] = None
+    fa_in_channel: Optional[str] = None
+    fa_hor_channel: Optional[str] = None
+    ca_in_channel: Optional[str] = None
+    aperture_config: Optional[dict[datetime, dict]] = None
+    lens_mode_config: Optional[dict[str, dict]] = None
+
+
+class NexusModel(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    # Currently only mpes reader is supported
+    reader: Literal["mpes"]
+    # Currently only NXmpes definition is supported
+    definition: Literal["NXmpes"]
+    input_files: Sequence[FilePath]
+
+
+class ConfigModel(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    core: CoreModel
+    dataframe: DataframeModel
+    energy: EnergyModel
+    momentum: MomentumModel
+    delay: DelayModel
+    binning: BinningModel
+    histogram: HistogramModel
+    metadata: Optional[MetadataModel] = None
+    nexus: Optional[NexusModel] = None
diff --git a/sed/core/dfops.py b/src/sed/core/dfops.py
similarity index 73%
rename from sed/core/dfops.py
rename to src/sed/core/dfops.py
index 4c428c90..d92d177e 100644
--- a/sed/core/dfops.py
+++ b/src/sed/core/dfops.py
@@ -3,9 +3,10 @@
 """
 # Note: some of the functions presented here were
 # inspired by https://github.com/mpes-kit/mpes
+from __future__ import annotations
+
+from collections.abc import Sequence
 from typing import Callable
-from typing import Sequence
-from typing import Union
 
 import dask.dataframe
 import numpy as np
@@ -14,21 +15,21 @@
 
 
 def apply_jitter(
-    df: Union[pd.DataFrame, dask.dataframe.DataFrame],
-    cols: Union[str, Sequence[str]],
-    cols_jittered: Union[str, Sequence[str]] = None,
-    amps: Union[float, Sequence[float]] = 0.5,
+    df: pd.DataFrame | dask.dataframe.DataFrame,
+    cols: str | Sequence[str],
+    cols_jittered: str | Sequence[str] = None,
+    amps: float | Sequence[float] = 0.5,
     jitter_type: str = "uniform",
-) -> Union[pd.DataFrame, dask.dataframe.DataFrame]:
+) -> pd.DataFrame | dask.dataframe.DataFrame:
     """Add jittering to one or more dataframe columns.
 
     Args:
-        df (Union[pd.DataFrame, dask.dataframe.DataFrame]): Dataframe to add
+        df (pd.DataFrame | dask.dataframe.DataFrame): Dataframe to add
             noise/jittering to.
-        cols (Union[str, Sequence[str]]): Names of the columns to add jittering to.
-        cols_jittered (Union[str, Sequence[str]], optional): Names of the columns
+        cols (str | Sequence[str]): Names of the columns to add jittering to.
+        cols_jittered (str | Sequence[str], optional): Names of the columns
             with added jitter. Defaults to None.
-        amps (Union[float, Sequence[float]], optional): Amplitude scalings for the
+        amps (float | Sequence[float], optional): Amplitude scalings for the
             jittering noise. If one number is given, the same is used for all axes.
             For normal noise, the added noise will have stdev [-amp, +amp], for
             uniform noise it will cover the interval [-amp, +amp].
@@ -37,7 +38,7 @@ def apply_jitter(
             distributed noise. Defaults to "uniform".
 
     Returns:
-        Union[pd.DataFrame, dask.dataframe.DataFrame]: dataframe with added columns.
+        pd.DataFrame | dask.dataframe.DataFrame: dataframe with added columns.
     """
     assert cols is not None, "cols needs to be provided!"
     assert jitter_type in (
@@ -71,17 +72,17 @@ def apply_jitter(
 
 
 def drop_column(
-    df: Union[pd.DataFrame, dask.dataframe.DataFrame],
-    column_name: Union[str, Sequence[str]],
-) -> Union[pd.DataFrame, dask.dataframe.DataFrame]:
+    df: pd.DataFrame | dask.dataframe.DataFrame,
+    column_name: str | Sequence[str],
+) -> pd.DataFrame | dask.dataframe.DataFrame:
     """Delete columns.
 
     Args:
-        df (Union[pd.DataFrame, dask.dataframe.DataFrame]): Dataframe to use.
-        column_name (Union[str, Sequence[str]])): List of column names to be dropped.
+        df (pd.DataFrame | dask.dataframe.DataFrame): Dataframe to use.
+        column_name (str | Sequence[str]): List of column names to be dropped.
 
     Returns:
-        Union[pd.DataFrame, dask.dataframe.DataFrame]: Dataframe with dropped columns.
+        pd.DataFrame | dask.dataframe.DataFrame: Dataframe with dropped columns.
     """
     out_df = df.drop(column_name, axis=1)
 
@@ -89,15 +90,15 @@ def drop_column(
 
 
 def apply_filter(
-    df: Union[pd.DataFrame, dask.dataframe.DataFrame],
+    df: pd.DataFrame | dask.dataframe.DataFrame,
     col: str,
     lower_bound: float = -np.inf,
     upper_bound: float = np.inf,
-) -> Union[pd.DataFrame, dask.dataframe.DataFrame]:
+) -> pd.DataFrame | dask.dataframe.DataFrame:
     """Application of bound filters to a specified column (can be used consecutively).
 
     Args:
-        df (Union[pd.DataFrame, dask.dataframe.DataFrame]): Dataframe to use.
+        df (pd.DataFrame | dask.dataframe.DataFrame): Dataframe to use.
         col (str): Name of the column to filter. Passing "index" for col will
             filter on the index in each dataframe partition.
         lower_bound (float, optional): The lower bound used in the filtering.
@@ -106,7 +107,7 @@ def apply_filter(
             Defaults to np.inf.
 
     Returns:
-        Union[pd.DataFrame, dask.dataframe.DataFrame]: The filtered dataframe.
+        pd.DataFrame | dask.dataframe.DataFrame: The filtered dataframe.
     """
     df = df.copy()
     if col == "index":
@@ -132,14 +133,15 @@ def add_time_stamped_data(
     timestamps in the dataframe.
 
     Args:
-        df (Union[pd.DataFrame, dask.dataframe.DataFrame]): Dataframe to use.
+        df (dask.dataframe.DataFrame): Dataframe to use.
         time_stamps (np.ndarray): Time stamps of the values to add
         data (np.ndarray): Values corresponding at the time stamps in time_stamps
         dest_column (str): destination column name
         time_stamp_column (str): Time stamp column name
+        **kwds: Keyword arguments passed to map_partitions
 
     Returns:
-        Union[pd.DataFrame, dask.dataframe.DataFrame]: Dataframe with added column
+        dask.dataframe.DataFrame: Dataframe with added column
     """
     if time_stamp_column not in df.columns:
         raise ValueError(f"{time_stamp_column} not found in dataframe!")
@@ -163,23 +165,27 @@ def interpolate_timestamps(
 
 
 def map_columns_2d(
-    df: Union[pd.DataFrame, dask.dataframe.DataFrame],
+    df: pd.DataFrame | dask.dataframe.DataFrame,
     map_2d: Callable,
     x_column: str,
     y_column: str,
     **kwds,
-) -> Union[pd.DataFrame, dask.dataframe.DataFrame]:
+) -> pd.DataFrame | dask.dataframe.DataFrame:
     """Apply a 2-dimensional mapping simultaneously to two dimensions.
 
     Args:
-        df (Union[pd.DataFrame, dask.dataframe.DataFrame]): Dataframe to use.
+        df (pd.DataFrame | dask.dataframe.DataFrame): Dataframe to use.
         map_2d (Callable): 2D mapping function.
         x_column (str): The X column of the dataframe to apply mapping to.
         y_column (str): The Y column of the dataframe to apply mapping to.
-        **kwds: Additional arguments for the 2D mapping function.
+        **kwds:
+            - *new_x_column*: Name of the new x-column. Default is to overwrite the x-column.
+            - *new_y_column*: Name of the new y-column. Default is to overwrite the y-column.
+
+            Additional keyword argument are passed to the 2D mapping function.
 
     Returns:
-        Union[pd.DataFrame, dask.dataframe.DataFrame]: Dataframe with mapped columns.
+        pd.DataFrame | dask.dataframe.DataFrame: Dataframe with mapped columns.
     """
     new_x_column = kwds.pop("new_x_column", x_column)
     new_y_column = kwds.pop("new_y_column", y_column)
@@ -196,7 +202,7 @@ def map_columns_2d(
 def forward_fill_lazy(
     df: dask.dataframe.DataFrame,
     columns: Sequence[str] = None,
-    before: Union[str, int] = "max",
+    before: str | int = "max",
     compute_lengths: bool = False,
     iterations: int = 2,
 ) -> dask.dataframe.DataFrame:
@@ -210,8 +216,8 @@ def forward_fill_lazy(
 
     Args:
         df (dask.dataframe.DataFrame): The dataframe to forward fill.
-        columns (list): The columns to forward fill. If None, fills all columns
-        before (int, str, optional): The number of rows to include before the current partition.
+        columns (list, optional): The columns to forward fill. If None, fills all columns
+        before (str | int, optional): The number of rows to include before the current partition.
             if 'max' it takes as much as possible from the previous partition, which is
             the size of the smallest partition in the dataframe. Defaults to 'max'.
         compute_lengths (bool, optional): Whether to compute the length of each partition
@@ -258,7 +264,7 @@ def forward_fill_partition(df):
 def backward_fill_lazy(
     df: dask.dataframe.DataFrame,
     columns: Sequence[str] = None,
-    after: Union[str, int] = "max",
+    after: str | int = "max",
     compute_lengths: bool = False,
     iterations: int = 1,
 ) -> dask.dataframe.DataFrame:
@@ -270,8 +276,8 @@ def backward_fill_lazy(
 
     Args:
         df (dask.dataframe.DataFrame): The dataframe to forward fill.
-        columns (list): The columns to forward fill. If None, fills all columns
-        after (int, str, optional): The number of rows to include after the current partition.
+        columns (list, optional): The columns to forward fill. If None, fills all columns
+        after (str | int, optional): The number of rows to include after the current partition.
             if 'max' it takes as much as possible from the previous partition, which is
             the size of the smallest partition in the dataframe. Defaults to 'max'.
         compute_lengths (bool, optional): Whether to compute the length of each partition
@@ -318,10 +324,10 @@ def backward_fill_partition(df):
 def offset_by_other_columns(
     df: dask.dataframe.DataFrame,
     target_column: str,
-    offset_columns: Union[str, Sequence[str]],
-    weights: Union[float, Sequence[float]],
-    reductions: Union[str, Sequence[str]] = None,
-    preserve_mean: Union[bool, Sequence[bool]] = False,
+    offset_columns: str | Sequence[str],
+    weights: float | Sequence[float],
+    reductions: str | Sequence[str] = None,
+    preserve_mean: bool | Sequence[bool] = False,
     inplace: bool = True,
     rename: str = None,
 ) -> dask.dataframe.DataFrame:
@@ -330,13 +336,13 @@ def offset_by_other_columns(
     Args:
         df (dask.dataframe.DataFrame): Dataframe to use. Currently supports only dask dataframes.
         target_column (str): Name of the column to apply the offset to.
-        offset_columns (str): Name of the column(s) to use for the offset.
-        weights (Union[float, Sequence[float]]): weights to apply on each column before adding.
-            Used also for changing sign.
-        reductions (Union[str, Sequence[str]], optional): Reduction function to use for the offset.
+        offset_columns (str | Sequence[str]): Name of the column(s) to use for the offset.
+        weights (float | Sequence[float]): weights to apply on each column before adding. Used also
+            for changing sign.
+        reductions (str | Sequence[str], optional): Reduction function to use for the offset.
             Defaults to "mean". Currently, only mean is supported.
-        preserve_mean (Union[bool, Sequence[bool]], optional): Whether to subtract the mean of the
-            offset column. Defaults to False. If a list is given, it must have the same length as
+        preserve_mean (bool | Sequence[bool], optional): Whether to subtract the mean of the offset
+            column. Defaults to False. If a list is given, it must have the same length as
             offset_columns. Otherwise the value passed is used for all columns.
         inplace (bool, optional): Whether to apply the offset inplace.
             If false, the new column will have the name provided by rename, or has the same name as
@@ -391,53 +397,20 @@ def offset_by_other_columns(
             "Please open a request on GitHub if this feature is required.",
         )
 
-    # calculate the mean of the columns to reduce
-    means = {
-        col: dask.delayed(df[col].mean())
-        for col, red, pm in zip(offset_columns, reductions, preserve_mean)
-        if red or pm
-    }
-
-    # define the functions to apply the offsets
-    def shift_by_mean(x, cols, signs, means, flip_signs=False):
-        """Shift the target column by the mean of the offset columns."""
-        for col in cols:
-            s = -signs[col] if flip_signs else signs[col]
-            x[target_column] = x[target_column] + s * means[col]
-        return x[target_column]
-
-    def shift_by_row(x, cols, signs):
-        """Apply the offsets to the target column."""
-        for col in cols:
-            x[target_column] = x[target_column] + signs[col] * x[col]
-        return x[target_column]
-
     # apply offset from the reduced columns
-    df[target_column] = df.map_partitions(
-        shift_by_mean,
-        cols=[col for col, red in zip(offset_columns, reductions) if red],
-        signs=signs_dict,
-        means=means,
-        meta=df[target_column].dtype,
-    )
+    for col, red in zip(offset_columns, reductions):
+        if red == "mean":
+            df[target_column] = df[target_column] + signs_dict[col] * df[col].mean()
 
     # apply offset from the offset columns
-    df[target_column] = df.map_partitions(
-        shift_by_row,
-        cols=[col for col, red in zip(offset_columns, reductions) if not red],
-        signs=signs_dict,
-        meta=df[target_column].dtype,
-    )
+    for col, red in zip(offset_columns, reductions):
+        if not red:
+            df[target_column] = df[target_column] + signs_dict[col] * df[col]
 
     # compensate shift from the preserved mean columns
     if any(preserve_mean):
-        df[target_column] = df.map_partitions(
-            shift_by_mean,
-            cols=[col for col, pmean in zip(offset_columns, preserve_mean) if pmean],
-            signs=signs_dict,
-            means=means,
-            flip_signs=True,
-            meta=df[target_column].dtype,
-        )
+        for col, pmean in zip(offset_columns, preserve_mean):
+            if pmean:
+                df[target_column] = df[target_column] - signs_dict[col] * df[col].mean()
 
     return df
diff --git a/src/sed/core/logging.py b/src/sed/core/logging.py
new file mode 100644
index 00000000..ad019dc8
--- /dev/null
+++ b/src/sed/core/logging.py
@@ -0,0 +1,122 @@
+"""
+This module provides a function to set up logging for the application. It configures
+both console and file logging handlers, allowing different log levels for each. The
+log files are stored in a user-specific log directory.
+
+"""
+from __future__ import annotations
+
+import logging
+import os
+import sys
+from datetime import datetime
+from functools import wraps
+from typing import Callable
+
+# Default log directory
+DEFAULT_LOG_DIR = os.path.join(os.getcwd(), "logs")
+CONSOLE_VERBOSITY = logging.INFO
+FILE_VERBOSITY = logging.DEBUG
+
+
+def setup_logging(
+    name: str,
+    set_base_handler: bool = False,
+    user_log_path: str | None = None,
+) -> logging.Logger:
+    """
+    Configures and returns a logger with specified log levels for console and file handlers.
+
+    Args:
+        name (str): The name of the logger.
+        set_base_handler (bool, optional): Option to re-initialize the base handler logging to the
+            logfile. Defaults to False.
+        user_log_path (str, optional): Path to the user-specific log directory.
+            Defaults to DEFAULT_LOG_DIR.
+
+    Returns:
+        logging.Logger: The configured logger instance.
+
+    The logger will always write DEBUG level messages to a file located in the user's log
+    directory, while the console log level can be adjusted based on the 'verbosity' parameter.
+    """
+    # Create base logger
+    base_logger = logging.getLogger("sed")
+    base_logger.setLevel(logging.DEBUG)  # Set the minimum log level for the logger
+    if set_base_handler or not base_logger.hasHandlers():
+        if base_logger.hasHandlers():
+            base_logger.handlers.clear()
+
+        # Determine log file path
+        if user_log_path is None:
+            user_log_path = DEFAULT_LOG_DIR
+        os.makedirs(user_log_path, exist_ok=True)
+        log_file = os.path.join(user_log_path, f"sed_{datetime.now().strftime('%Y-%m-%d')}.log")
+
+        # Create file handler and set level to debug
+        file_handler = logging.FileHandler(log_file)
+        file_handler.setLevel(FILE_VERBOSITY)
+
+        # Create formatter for file
+        file_formatter = logging.Formatter(
+            "%(asctime)s - %(name)s - %(levelname)s - %(message)s in %(filename)s:%(lineno)d",
+        )
+        file_handler.setFormatter(file_formatter)
+
+        # Add file handler to logger
+        base_logger.addHandler(file_handler)
+
+    # create named logger
+    logger = base_logger.getChild(name)
+
+    if logger.hasHandlers():
+        logger.handlers.clear()
+
+    # Create console handler and set level
+    console_handler = logging.StreamHandler(sys.stdout)
+    console_handler.setLevel(CONSOLE_VERBOSITY)
+
+    # Create formatter for console
+    console_formatter = logging.Formatter("%(levelname)s - %(message)s")
+    console_handler.setFormatter(console_formatter)
+
+    # Add console handler to logger
+    logger.addHandler(console_handler)
+
+    # Capture warnings with the logging system
+    logging.captureWarnings(True)
+
+    return logger
+
+
+def set_verbosity(logger: logging.Logger, verbose: bool) -> None:
+    """Sets log level for the given logger's default handler.
+
+    Args:
+        logger (logging.Logger): The logger on which to set the log level.
+        verbose (bool): Sets loglevel to INFO if True, to WARNING otherwise.
+    """
+    handler = logger.handlers[0]
+    if verbose:
+        handler.setLevel(logging.INFO)
+    else:
+        handler.setLevel(logging.WARNING)
+
+
+def call_logger(logger: logging.Logger):
+    def log_call(func: Callable):
+        @wraps(func)
+        def new_func(*args, **kwargs):
+            saved_args = locals()
+            args_str = ""
+            for arg in saved_args["args"][1:]:
+                args_str += f"{arg}, "
+            for name, arg in saved_args["kwargs"].items():
+                args_str += f"{name}={arg}, "
+            args_str = args_str.rstrip(", ")
+            logger.debug(f"Call {func.__name__}({args_str})")
+            return func(*args, **kwargs)
+
+        return new_func
+
+    return log_call
diff --git a/sed/core/metadata.py b/src/sed/core/metadata.py
similarity index 76%
rename from sed/core/metadata.py
rename to src/sed/core/metadata.py
index 25f501ea..d930d2a3 100644
--- a/sed/core/metadata.py
+++ b/src/sed/core/metadata.py
@@ -1,30 +1,64 @@
 """This is a metadata handler class from the sed package
 """
+from __future__ import annotations
+
 import json
 from copy import deepcopy
 from typing import Any
-from typing import Dict
 
 from sed.core.config import complete_dictionary
 
 
 class MetaHandler:
     """This class provides methods to manipulate metadata dictionaries,
-    and give a nice representation of them."""
+    and give a nice representation of them.
+
+    Args:
+        meta (dict, optional): Pre-existing metadata dict. Defaults to None.
+    """
 
-    def __init__(self, meta: Dict = None) -> None:
+    def __init__(self, meta: dict = None) -> None:
+        """Constructor.
+
+        Args:
+            meta (dict, optional): Pre-existing metadata dict. Defaults to None.
+        """
         self._m = deepcopy(meta) if meta is not None else {}
 
-    def __getitem__(self, val: Any) -> None:
+    def __getitem__(self, val: Any) -> Any:
+        """Function for getting a value
+
+        Args:
+            val (Any): Metadata category key
+
+        Returns:
+            Any: The metadata category entry.
+        """
         return self._m[val]
 
     def __repr__(self) -> str:
+        """String representation function as json
+
+        Returns:
+            str: Summary string.
+        """
         return json.dumps(self._m, default=str, indent=4)
 
-    def _format_attributes(self, attributes, indent=0):
+    def _format_attributes(self, attributes: dict, indent: int = 0) -> str:
+        """Function to summarize a dictionary as html
+
+        Args:
+            attributes (dict): dictionary to summarize
+            indent (int, optional): Indentation value. Defaults to 0.
+
+        Returns:
+            str: Generated html summary.
+        """
         INDENT_FACTOR = 20
         html = ""
         for key, value in attributes.items():
+            # Ensure the key is a string
+            key = str(key)
             # Format key
             formatted_key = key.replace("_", " ").title()
             formatted_key = f"<b>{formatted_key}</b>"
@@ -42,11 +76,16 @@ def _format_attributes(self, attributes, indent=0):
         return html
 
     def _repr_html_(self) -> str:
+        """Summary function as html
+
+        Returns:
+            str: Generated html summary
+        """
         html = self._format_attributes(self._m)
         return html
 
     @property
-    def metadata(self) -> Dict:
+    def metadata(self) -> dict:
         """Property returning the metadata dict.
         Returns:
             dict: Dictionary of metadata.
diff --git a/sed/core/processor.py b/src/sed/core/processor.py
similarity index 79%
rename from sed/core/processor.py
rename to src/sed/core/processor.py
index 8e14161a..2345f206 100644
--- a/sed/core/processor.py
+++ b/src/sed/core/processor.py
@@ -1,15 +1,14 @@
 """This module contains the core class for the sed package
 
 """
+from __future__ import annotations
+
 import pathlib
+from collections.abc import Sequence
+from copy import deepcopy
 from datetime import datetime
 from typing import Any
 from typing import cast
-from typing import Dict
-from typing import List
-from typing import Sequence
-from typing import Tuple
-from typing import Union
 
 import dask.dataframe as ddf
 import matplotlib.pyplot as plt
@@ -29,6 +28,9 @@
 from sed.core.dfops import add_time_stamped_data
 from sed.core.dfops import apply_filter
 from sed.core.dfops import apply_jitter
+from sed.core.logging import call_logger
+from sed.core.logging import set_verbosity
+from sed.core.logging import setup_logging
 from sed.core.metadata import MetaHandler
 from sed.diagnostics import grid_histogram
 from sed.io import to_h5
@@ -41,6 +43,9 @@
 
 N_CPU = psutil.cpu_count()
 
+# Configure logging
+logger = setup_logging("processor")
+
 
 class SedProcessor:
     """Processor class of sed. Contains wrapper functions defining a work flow for data
@@ -48,11 +53,11 @@ class SedProcessor:
 
     Args:
         metadata (dict, optional): Dict of external Metadata. Defaults to None.
-        config (Union[dict, str], optional): Config dictionary or config file name.
+        config (dict | str, optional): Config dictionary or config file name.
             Defaults to None.
-        dataframe (Union[pd.DataFrame, ddf.DataFrame], optional): dataframe to load
+        dataframe (pd.DataFrame | ddf.DataFrame, optional): dataframe to load
             into the class. Defaults to None.
-        files (List[str], optional): List of files to pass to the loader defined in
+        files (list[str], optional): List of files to pass to the loader defined in
             the config. Defaults to None.
         folder (str, optional): Folder containing files to pass to the loader
             defined in the config. Defaults to None.
@@ -61,16 +66,17 @@ class SedProcessor:
         collect_metadata (bool): Option to collect metadata from files.
             Defaults to False.
         verbose (bool, optional): Option to print out diagnostic information.
-            Defaults to config["core"]["verbose"] or False.
+            Defaults to config["core"]["verbose"] or True.
         **kwds: Keyword arguments passed to the reader.
     """
 
+    @call_logger(logger)
     def __init__(
         self,
         metadata: dict = None,
-        config: Union[dict, str] = None,
-        dataframe: Union[pd.DataFrame, ddf.DataFrame] = None,
-        files: List[str] = None,
+        config: dict | str = None,
+        dataframe: pd.DataFrame | ddf.DataFrame = None,
+        files: list[str] = None,
         folder: str = None,
         runs: Sequence[str] = None,
         collect_metadata: bool = False,
@@ -82,11 +88,11 @@ def __init__(
 
         Args:
             metadata (dict, optional): Dict of external Metadata. Defaults to None.
-            config (Union[dict, str], optional): Config dictionary or config file name.
+            config (dict | str, optional): Config dictionary or config file name.
                 Defaults to None.
-            dataframe (Union[pd.DataFrame, ddf.DataFrame], optional): dataframe to load
+            dataframe (pd.DataFrame | ddf.DataFrame, optional): dataframe to load
                 into the class. Defaults to None.
-            files (List[str], optional): List of files to pass to the loader defined in
+            files (list[str], optional): List of files to pass to the loader defined in
                 the config. Defaults to None.
             folder (str, optional): Folder containing files to pass to the loader
                 defined in the config. Defaults to None.
@@ -95,28 +101,31 @@ def __init__(
             collect_metadata (bool, optional): Option to collect metadata from files.
                 Defaults to False.
             verbose (bool, optional): Option to print out diagnostic information.
-                Defaults to config["core"]["verbose"] or False.
+                Defaults to config["core"]["verbose"] or True.
             **kwds: Keyword arguments passed to parse_config and to the reader.
         """
+        # split off config keywords
         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)
+        num_cores = self._config["core"].get("num_cores", N_CPU - 1)
         if num_cores >= N_CPU:
             num_cores = N_CPU - 1
-        self._config["binning"]["num_cores"] = num_cores
+        self._config["core"]["num_cores"] = num_cores
+        logger.debug(f"Use {num_cores} cores for processing.")
 
         if verbose is None:
-            self.verbose = self._config["core"].get("verbose", False)
+            self._verbose = self._config["core"].get("verbose", True)
         else:
-            self.verbose = verbose
+            self._verbose = verbose
+        set_verbosity(logger, self._verbose)
 
-        self._dataframe: Union[pd.DataFrame, ddf.DataFrame] = None
-        self._timed_dataframe: Union[pd.DataFrame, ddf.DataFrame] = None
-        self._files: List[str] = []
+        self._dataframe: pd.DataFrame | ddf.DataFrame = None
+        self._timed_dataframe: pd.DataFrame | ddf.DataFrame = None
+        self._files: list[str] = []
 
         self._binned: xr.DataArray = None
         self._pre_binned: xr.DataArray = None
@@ -129,34 +138,41 @@ def __init__(
         self.loader = get_loader(
             loader_name=loader_name,
             config=self._config,
+            verbose=verbose,
         )
+        logger.debug(f"Use loader: {loader_name}")
 
         self.ec = EnergyCalibrator(
             loader=get_loader(
                 loader_name=loader_name,
                 config=self._config,
+                verbose=verbose,
             ),
             config=self._config,
+            verbose=self._verbose,
         )
 
         self.mc = MomentumCorrector(
             config=self._config,
+            verbose=self._verbose,
         )
 
         self.dc = DelayCalibrator(
             config=self._config,
+            verbose=self._verbose,
         )
 
-        self.use_copy_tool = self._config.get("core", {}).get(
-            "use_copy_tool",
-            False,
-        )
+        self.use_copy_tool = "copy_tool" in self._config["core"]
         if self.use_copy_tool:
             try:
                 self.ct = CopyTool(
-                    source=self._config["core"]["copy_tool_source"],
-                    dest=self._config["core"]["copy_tool_dest"],
-                    **self._config["core"].get("copy_tool_kwds", {}),
+                    num_cores=self._config["core"]["num_cores"],
+                    **self._config["core"]["copy_tool"],
+                )
+                logger.debug(
+                    f"Initialized copy tool: Copy files from "
+                    f"'{self._config['core']['copy_tool']['source']}' "
+                    f"to '{self._config['core']['copy_tool']['dest']}'.",
                 )
             except KeyError:
                 self.use_copy_tool = False
@@ -208,20 +224,43 @@ def _repr_html_(self):
     #     self.view_event_histogram(dfpid=2, backend="matplotlib")
 
     @property
-    def dataframe(self) -> Union[pd.DataFrame, ddf.DataFrame]:
+    def verbose(self) -> bool:
+        """Accessor to the verbosity flag.
+
+        Returns:
+            bool: Verbosity flag.
+        """
+        return self._verbose
+
+    @verbose.setter
+    def verbose(self, verbose: bool):
+        """Setter for the verbosity.
+
+        Args:
+            verbose (bool): Option to turn on verbose output. Sets loglevel to INFO.
+        """
+        self._verbose = verbose
+        set_verbosity(logger, self._verbose)
+        self.mc.verbose = verbose
+        self.ec.verbose = verbose
+        self.dc.verbose = verbose
+        self.loader.verbose = verbose
+
+    @property
+    def dataframe(self) -> pd.DataFrame | ddf.DataFrame:
         """Accessor to the underlying dataframe.
 
         Returns:
-            Union[pd.DataFrame, ddf.DataFrame]: Dataframe object.
+            pd.DataFrame | ddf.DataFrame: Dataframe object.
         """
         return self._dataframe
 
     @dataframe.setter
-    def dataframe(self, dataframe: Union[pd.DataFrame, ddf.DataFrame]):
+    def dataframe(self, dataframe: pd.DataFrame | ddf.DataFrame):
         """Setter for the underlying dataframe.
 
         Args:
-            dataframe (Union[pd.DataFrame, ddf.DataFrame]): The dataframe object to set.
+            dataframe (pd.DataFrame | ddf.DataFrame): The dataframe object to set.
         """
         if not isinstance(dataframe, (pd.DataFrame, ddf.DataFrame)) or not isinstance(
             dataframe,
@@ -235,20 +274,20 @@ def dataframe(self, dataframe: Union[pd.DataFrame, ddf.DataFrame]):
         self._dataframe = dataframe
 
     @property
-    def timed_dataframe(self) -> Union[pd.DataFrame, ddf.DataFrame]:
+    def timed_dataframe(self) -> pd.DataFrame | ddf.DataFrame:
         """Accessor to the underlying timed_dataframe.
 
         Returns:
-            Union[pd.DataFrame, ddf.DataFrame]: Timed Dataframe object.
+            pd.DataFrame | ddf.DataFrame: Timed Dataframe object.
         """
         return self._timed_dataframe
 
     @timed_dataframe.setter
-    def timed_dataframe(self, timed_dataframe: Union[pd.DataFrame, ddf.DataFrame]):
+    def timed_dataframe(self, timed_dataframe: pd.DataFrame | ddf.DataFrame):
         """Setter for the underlying timed dataframe.
 
         Args:
-            timed_dataframe (Union[pd.DataFrame, ddf.DataFrame]): The timed dataframe object to set
+            timed_dataframe (pd.DataFrame | ddf.DataFrame): The timed dataframe object to set
         """
         if not isinstance(timed_dataframe, (pd.DataFrame, ddf.DataFrame)) or not isinstance(
             timed_dataframe,
@@ -277,6 +316,7 @@ def add_attribute(self, attributes: dict, name: str, **kwds):
         Args:
             attributes (dict): The attributes dictionary object to add.
             name (str): Key under which to add the dictionary to the attributes.
+            **kwds: Additional keywords are passed to the ``MetaHandler.add()`` function.
         """
         self._attributes.add(
             entry=attributes,
@@ -285,20 +325,20 @@ def add_attribute(self, attributes: dict, name: str, **kwds):
         )
 
     @property
-    def config(self) -> Dict[Any, Any]:
+    def config(self) -> dict[Any, Any]:
         """Getter attribute for the config dictionary
 
         Returns:
-            Dict: The config dictionary.
+            dict: The config dictionary.
         """
         return self._config
 
     @property
-    def files(self) -> List[str]:
+    def files(self) -> list[str]:
         """Getter attribute for the list of files
 
         Returns:
-            List[str]: The list of loaded files
+            list[str]: The list of loaded files
         """
         return self._files
 
@@ -337,17 +377,17 @@ def normalization_histogram(self) -> xr.DataArray:
             raise ValueError("No normalization histogram available, generate histogram first!")
         return self._normalization_histogram
 
-    def cpy(self, path: Union[str, List[str]]) -> Union[str, List[str]]:
+    def cpy(self, path: str | list[str]) -> str | list[str]:
         """Function to mirror a list of files or a folder from a network drive to a
         local storage. Returns either the original or the copied path to the given
         path. The option to use this functionality is set by
         config["core"]["use_copy_tool"].
 
         Args:
-            path (Union[str, List[str]]): Source path or path list.
+            path (str | list[str]): Source path or path list.
 
         Returns:
-            Union[str, List[str]]: Source or destination path or path list.
+            str | list[str]: Source or destination path or path list.
         """
         if self.use_copy_tool:
             if isinstance(path, list):
@@ -363,11 +403,12 @@ def cpy(self, path: Union[str, List[str]]) -> Union[str, List[str]]:
 
         return path
 
+    @call_logger(logger)
     def load(
         self,
-        dataframe: Union[pd.DataFrame, ddf.DataFrame] = None,
+        dataframe: pd.DataFrame | ddf.DataFrame = None,
         metadata: dict = None,
-        files: List[str] = None,
+        files: list[str] = None,
         folder: str = None,
         runs: Sequence[str] = None,
         collect_metadata: bool = False,
@@ -376,18 +417,21 @@ def load(
         """Load tabular data of single events into the dataframe object in the class.
 
         Args:
-            dataframe (Union[pd.DataFrame, ddf.DataFrame], optional): data in tabular
+            dataframe (pd.DataFrame | ddf.DataFrame, optional): data in tabular
                 format. Accepts anything which can be interpreted by pd.DataFrame as
                 an input. Defaults to None.
             metadata (dict, optional): Dict of external Metadata. Defaults to None.
-            files (List[str], optional): List of file paths to pass to the loader.
+            files (list[str], optional): List of file paths to pass to the loader.
                 Defaults to None.
             runs (Sequence[str], optional): List of run identifiers to pass to the
                 loader. Defaults to None.
             folder (str, optional): Folder path to pass to the loader.
                 Defaults to None.
             collect_metadata (bool, optional): Option for collecting metadata in the reader.
-            **kwds: Keyword parameters passed to the reader.
+            **kwds:
+                - *timed_dataframe*: timed dataframe if dataframe is provided.
+
+                Additional keyword parameters are passed to ``loader.read_dataframe()``.
 
         Raises:
             ValueError: Raised if no valid input is provided.
@@ -425,7 +469,7 @@ def load(
             )
         elif files is not None:
             dataframe, timed_dataframe, metadata = self.loader.read_dataframe(
-                files=cast(List[str], self.cpy(files)),
+                files=cast(list[str], self.cpy(files)),
                 metadata=metadata,
                 collect_metadata=collect_metadata,
                 **kwds,
@@ -446,6 +490,7 @@ def load(
                 duplicate_policy="merge",
             )
 
+    @call_logger(logger)
     def filter_column(
         self,
         column: str,
@@ -488,12 +533,13 @@ def filter_column(
 
     # Momentum calibration workflow
     # 1. Bin raw detector data for distortion correction
+    @call_logger(logger)
     def bin_and_load_momentum_calibration(
         self,
-        df_partitions: Union[int, Sequence[int]] = 100,
-        axes: List[str] = None,
-        bins: List[int] = None,
-        ranges: Sequence[Tuple[float, float]] = None,
+        df_partitions: int | Sequence[int] = 100,
+        axes: list[str] = None,
+        bins: list[int] = None,
+        ranges: Sequence[tuple[float, float]] = None,
         plane: int = 0,
         width: int = 5,
         apply: bool = False,
@@ -504,13 +550,13 @@ def bin_and_load_momentum_calibration(
         interactive view, and load it into the momentum corrector class.
 
         Args:
-            df_partitions (Union[int, Sequence[int]], optional): Number of dataframe partitions
+            df_partitions (int | Sequence[int], optional): Number of dataframe partitions
                 to use for the initial binning. Defaults to 100.
-            axes (List[str], optional): Axes to bin.
+            axes (list[str], optional): Axes to bin.
                 Defaults to config["momentum"]["axes"].
-            bins (List[int], optional): Bin numbers to use for binning.
+            bins (list[int], optional): Bin numbers to use for binning.
                 Defaults to config["momentum"]["bins"].
-            ranges (List[Tuple], optional): Ranges to use for binning.
+            ranges (Sequence[tuple[float, float]], optional): Ranges to use for binning.
                 Defaults to config["momentum"]["ranges"].
             plane (int, optional): Initial value for the plane slider. Defaults to 0.
             width (int, optional): Initial value for the width slider. Defaults to 5.
@@ -531,6 +577,7 @@ def bin_and_load_momentum_calibration(
 
     # 2. Generate the spline warp correction from momentum features.
     # Either autoselect features, or input features from view above.
+    @call_logger(logger)
     def define_features(
         self,
         features: np.ndarray = None,
@@ -584,10 +631,10 @@ def define_features(
 
     # 3. Generate the spline warp correction from momentum features.
     # If no features have been selected before, use class defaults.
+    @call_logger(logger)
     def generate_splinewarp(
         self,
         use_center: bool = None,
-        verbose: bool = None,
         **kwds,
     ):
         """3. Step of the distortion correction workflow: Generate the correction
@@ -596,34 +643,34 @@ def generate_splinewarp(
         Args:
             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.
-            verbose (bool, optional): Option to print out diagnostic information.
-                Defaults to config["core"]["verbose"].
             **kwds: Keyword arguments for MomentumCorrector.spline_warp_estimate().
         """
-        if verbose is None:
-            verbose = self.verbose
 
-        self.mc.spline_warp_estimate(use_center=use_center, verbose=verbose, **kwds)
+        self.mc.spline_warp_estimate(use_center=use_center, **kwds)
 
-        if self.mc.slice is not None and verbose:
-            print("Original slice with reference features")
-            self.mc.view(annotated=True, backend="bokeh", crosshair=True)
+        if self.mc.slice is not None and self._verbose:
+            self.mc.view(
+                annotated=True,
+                backend="matplotlib",
+                crosshair=True,
+                title="Original slice with reference features",
+            )
 
-            print("Corrected slice with target features")
             self.mc.view(
                 image=self.mc.slice_corrected,
                 annotated=True,
                 points={"feats": self.mc.ptargs},
-                backend="bokeh",
+                backend="matplotlib",
                 crosshair=True,
+                title="Corrected slice with target features",
             )
 
-            print("Original slice with target features")
             self.mc.view(
                 image=self.mc.slice,
                 points={"feats": self.mc.ptargs},
                 annotated=True,
-                backend="bokeh",
+                backend="matplotlib",
+                title="Original slice with target features",
             )
 
     # 3a. Save spline-warp parameters to config file.
@@ -656,11 +703,13 @@ def save_splinewarp(
                 correction[key] = []
                 for point in value:
                     correction[key].append([float(i) for i in point])
+            elif key == "creation_date":
+                correction[key] = value.isoformat()
             else:
                 correction[key] = float(value)
 
         if "creation_date" not in correction:
-            correction["creation_date"] = datetime.now().timestamp()
+            correction["creation_date"] = datetime.now().isoformat()
 
         config = {
             "momentum": {
@@ -668,17 +717,17 @@ def save_splinewarp(
             },
         }
         save_config(config, filename, overwrite)
-        print(f'Saved momentum correction parameters to "{filename}".')
+        logger.info(f'Saved momentum correction parameters to "{filename}".')
 
     # 4. Pose corrections. Provide interactive interface for correcting
     # scaling, shift and rotation
+    @call_logger(logger)
     def pose_adjustment(
         self,
-        transformations: Dict[str, Any] = None,
+        transformations: dict[str, Any] = None,
         apply: bool = False,
         use_correction: bool = True,
         reset: bool = True,
-        verbose: bool = None,
         **kwds,
     ):
         """3. step of the distortion correction workflow: Generate an interactive panel
@@ -687,7 +736,7 @@ def pose_adjustment(
         the image.
 
         Args:
-            transformations (dict, optional): Dictionary with transformations.
+            transformations (dict[str, Any], optional): Dictionary with transformations.
                 Defaults to self.transformations or config["momentum"]["transformations"].
             apply (bool, optional): Option to directly apply the provided
                 transformations. Defaults to False.
@@ -695,8 +744,6 @@ def pose_adjustment(
                 or not. Defaults to True.
             reset (bool, optional): Option to reset the correction before transformation.
                 Defaults to True.
-            verbose (bool, optional): Option to print out diagnostic information.
-                Defaults to config["core"]["verbose"].
             **kwds: Keyword parameters defining defaults for the transformations:
 
                 - **scale** (float): Initial value of the scaling slider.
@@ -704,9 +751,6 @@ def pose_adjustment(
                 - **ytrans** (float): Initial value of the ytrans slider.
                 - **angle** (float): Initial value of the angle slider.
         """
-        if verbose is None:
-            verbose = self.verbose
-
         # Generate homography as default if no distortion correction has been applied
         if self.mc.slice_corrected is None:
             if self.mc.slice is None:
@@ -719,17 +763,17 @@ def pose_adjustment(
 
         if self.mc.cdeform_field is None or self.mc.rdeform_field is None:
             # Generate distortion correction from config values
-            self.mc.spline_warp_estimate(verbose=verbose)
+            self.mc.spline_warp_estimate()
 
         self.mc.pose_adjustment(
             transformations=transformations,
             apply=apply,
             reset=reset,
-            verbose=verbose,
             **kwds,
         )
 
     # 4a. Save pose adjustment parameters to config file.
+    @call_logger(logger)
     def save_transformations(
         self,
         filename: str = None,
@@ -749,10 +793,13 @@ def save_transformations(
             raise ValueError("No momentum transformation parameters to save!")
         transformations = {}
         for key, value in self.mc.transformations.items():
-            transformations[key] = float(value)
+            if key == "creation_date":
+                transformations[key] = value.isoformat()
+            else:
+                transformations[key] = float(value)
 
         if "creation_date" not in transformations:
-            transformations["creation_date"] = datetime.now().timestamp()
+            transformations["creation_date"] = datetime.now().isoformat()
 
         config = {
             "momentum": {
@@ -760,13 +807,13 @@ def save_transformations(
             },
         }
         save_config(config, filename, overwrite)
-        print(f'Saved momentum transformation parameters to "{filename}".')
+        logger.info(f'Saved momentum transformation parameters to "{filename}".')
 
     # 5. Apply the momentum correction to the dataframe
+    @call_logger(logger)
     def apply_momentum_correction(
         self,
         preview: bool = False,
-        verbose: bool = None,
         **kwds,
     ):
         """Applies the distortion correction and pose adjustment (optional)
@@ -775,8 +822,6 @@ def apply_momentum_correction(
         Args:
             preview (bool, optional): Option to preview the first elements of the data frame.
                 Defaults to False.
-            verbose (bool, optional): Option to print out diagnostic information.
-                Defaults to config["core"]["verbose"].
             **kwds: Keyword parameters for ``MomentumCorrector.apply_correction``:
 
                 - **rdeform_field** (np.ndarray, optional): Row deformation field.
@@ -784,18 +829,13 @@ def apply_momentum_correction(
                 - **inv_dfield** (np.ndarray, optional): Inverse deformation field.
 
         """
-        if verbose is None:
-            verbose = self.verbose
-
-        x_column = self._config["dataframe"]["x_column"]
-        y_column = self._config["dataframe"]["y_column"]
+        x_column = self._config["dataframe"]["columns"]["x"]
+        y_column = self._config["dataframe"]["columns"]["y"]
 
         if self._dataframe is not None:
-            if verbose:
-                print("Adding corrected X/Y columns to dataframe:")
+            logger.info("Adding corrected X/Y columns to dataframe:")
             df, metadata = self.mc.apply_corrections(
                 df=self._dataframe,
-                verbose=verbose,
                 **kwds,
             )
             if (
@@ -805,7 +845,6 @@ def apply_momentum_correction(
             ):
                 tdf, _ = self.mc.apply_corrections(
                     self._timed_dataframe,
-                    verbose=False,
                     **kwds,
                 )
 
@@ -825,20 +864,20 @@ def apply_momentum_correction(
         else:
             raise ValueError("No dataframe loaded!")
         if preview:
-            print(self._dataframe.head(10))
+            logger.info(self._dataframe.head(10))
         else:
-            if self.verbose:
-                print(self._dataframe)
+            logger.info(self._dataframe)
 
     # Momentum calibration work flow
     # 1. Calculate momentum calibration
+    @call_logger(logger)
     def calibrate_momentum_axes(
         self,
-        point_a: Union[np.ndarray, List[int]] = None,
-        point_b: Union[np.ndarray, List[int]] = None,
+        point_a: np.ndarray | list[int] = None,
+        point_b: np.ndarray | list[int] = None,
         k_distance: float = None,
-        k_coord_a: Union[np.ndarray, List[float]] = None,
-        k_coord_b: Union[np.ndarray, List[float]] = np.array([0.0, 0.0]),
+        k_coord_a: np.ndarray | list[float] = None,
+        k_coord_b: np.ndarray | list[float] = np.array([0.0, 0.0]),
         equiscale: bool = True,
         apply=False,
     ):
@@ -849,18 +888,18 @@ def calibrate_momentum_axes(
         the points.
 
         Args:
-            point_a (Union[np.ndarray, List[int]]): Pixel coordinates of the first
+            point_a (np.ndarray | list[int], optional): Pixel coordinates of the first
                 point used for momentum calibration.
-            point_b (Union[np.ndarray, List[int]], optional): Pixel coordinates of the
+            point_b (np.ndarray | list[int], optional): Pixel coordinates of the
                 second point used for momentum calibration.
                 Defaults to config["momentum"]["center_pixel"].
             k_distance (float, optional): Momentum distance between point a and b.
                 Needs to be provided if no specific k-coordinates for the two points
                 are given. Defaults to None.
-            k_coord_a (Union[np.ndarray, List[float]], optional): Momentum coordinate
+            k_coord_a (np.ndarray | list[float], optional): Momentum coordinate
                 of the first point used for calibration. Used if equiscale is False.
                 Defaults to None.
-            k_coord_b (Union[np.ndarray, List[float]], optional): Momentum coordinate
+            k_coord_b (np.ndarray | list[float], optional): Momentum coordinate
                 of the second point used for calibration. Defaults to [0.0, 0.0].
             equiscale (bool, optional): Option to apply different scales to kx and ky.
                 If True, the distance between points a and b, and the absolute
@@ -905,22 +944,24 @@ def save_momentum_calibration(
         for key, value in self.mc.calibration.items():
             if key in ["kx_axis", "ky_axis", "grid", "extent"]:
                 continue
-
-            calibration[key] = float(value)
+            elif key == "creation_date":
+                calibration[key] = value.isoformat()
+            else:
+                calibration[key] = float(value)
 
         if "creation_date" not in calibration:
-            calibration["creation_date"] = datetime.now().timestamp()
+            calibration["creation_date"] = datetime.now().isoformat()
 
         config = {"momentum": {"calibration": calibration}}
         save_config(config, filename, overwrite)
-        print(f"Saved momentum calibration parameters to {filename}")
+        logger.info(f"Saved momentum calibration parameters to {filename}")
 
     # 2. Apply correction and calibration to the dataframe
+    @call_logger(logger)
     def apply_momentum_calibration(
         self,
         calibration: dict = None,
         preview: bool = False,
-        verbose: bool = None,
         **kwds,
     ):
         """2. step of the momentum calibration work flow: Apply the momentum
@@ -936,15 +977,11 @@ def apply_momentum_calibration(
                 Defaults to config["core"]["verbose"].
             **kwds: Keyword args passed to ``MomentumCorrector.append_k_axis``.
         """
-        if verbose is None:
-            verbose = self.verbose
-
-        x_column = self._config["dataframe"]["x_column"]
-        y_column = self._config["dataframe"]["y_column"]
+        x_column = self._config["dataframe"]["columns"]["x"]
+        y_column = self._config["dataframe"]["columns"]["y"]
 
         if self._dataframe is not None:
-            if verbose:
-                print("Adding kx/ky columns to dataframe:")
+            logger.info("Adding kx/ky columns to dataframe:")
             df, metadata = self.mc.append_k_axis(
                 df=self._dataframe,
                 calibration=calibration,
@@ -958,6 +995,7 @@ def apply_momentum_calibration(
                 tdf, _ = self.mc.append_k_axis(
                     df=self._timed_dataframe,
                     calibration=calibration,
+                    suppress_output=True,
                     **kwds,
                 )
 
@@ -977,18 +1015,18 @@ def apply_momentum_calibration(
         else:
             raise ValueError("No dataframe loaded!")
         if preview:
-            print(self._dataframe.head(10))
+            logger.info(self._dataframe.head(10))
         else:
-            if self.verbose:
-                print(self._dataframe)
+            logger.info(self._dataframe)
 
     # Energy correction workflow
     # 1. Adjust the energy correction parameters
+    @call_logger(logger)
     def adjust_energy_correction(
         self,
         correction_type: str = None,
         amplitude: float = None,
-        center: Tuple[float, float] = None,
+        center: tuple[float, float] = None,
         apply=False,
         **kwds,
     ):
@@ -1008,16 +1046,14 @@ def adjust_energy_correction(
                 Defaults to config["energy"]["correction_type"].
             amplitude (float, optional): Amplitude of the correction.
                 Defaults to config["energy"]["correction"]["amplitude"].
-            center (Tuple[float, float], optional): Center X/Y coordinates for the
+            center (tuple[float, float], optional): Center X/Y coordinates for the
                 correction. Defaults to config["energy"]["correction"]["center"].
             apply (bool, optional): Option to directly apply the provided or default
                 correction parameters. Defaults to False.
             **kwds: Keyword parameters passed to ``EnergyCalibrator.adjust_energy_correction()``.
         """
         if self._pre_binned is None:
-            print(
-                "Pre-binned data not present, binning using defaults from config...",
-            )
+            logger.warn("Pre-binned data not present, binning using defaults from config...")
             self._pre_binned = self.pre_binning()
 
         self.ec.adjust_energy_correction(
@@ -1048,27 +1084,29 @@ def save_energy_correction(
         if len(self.ec.correction) == 0:
             raise ValueError("No energy correction parameters to save!")
         correction = {}
-        for key, val in self.ec.correction.items():
+        for key, value in self.ec.correction.items():
             if key == "correction_type":
-                correction[key] = val
+                correction[key] = value
             elif key == "center":
-                correction[key] = [float(i) for i in val]
+                correction[key] = [float(i) for i in value]
+            elif key == "creation_date":
+                correction[key] = value.isoformat()
             else:
-                correction[key] = float(val)
+                correction[key] = float(value)
 
         if "creation_date" not in correction:
-            correction["creation_date"] = datetime.now().timestamp()
+            correction["creation_date"] = datetime.now().isoformat()
 
         config = {"energy": {"correction": correction}}
         save_config(config, filename, overwrite)
-        print(f"Saved energy correction parameters to {filename}")
+        logger.info(f"Saved energy correction parameters to {filename}")
 
     # 2. Apply energy correction to dataframe
+    @call_logger(logger)
     def apply_energy_correction(
         self,
         correction: dict = None,
         preview: bool = False,
-        verbose: bool = None,
         **kwds,
     ):
         """2. step of the energy correction workflow: Apply the energy correction
@@ -1079,30 +1117,23 @@ def apply_energy_correction(
                 parameters. Defaults to config["energy"]["calibration"].
             preview (bool, optional): Option to preview the first elements of the data frame.
                 Defaults to False.
-            verbose (bool, optional): Option to print out diagnostic information.
-                Defaults to config["core"]["verbose"].
             **kwds:
                 Keyword args passed to ``EnergyCalibrator.apply_energy_correction()``.
         """
-        if verbose is None:
-            verbose = self.verbose
-
-        tof_column = self._config["dataframe"]["tof_column"]
+        tof_column = self._config["dataframe"]["columns"]["tof"]
 
         if self._dataframe is not None:
-            if verbose:
-                print("Applying energy correction to dataframe...")
+            logger.info("Applying energy correction to dataframe...")
             df, metadata = self.ec.apply_energy_correction(
                 df=self._dataframe,
                 correction=correction,
-                verbose=verbose,
                 **kwds,
             )
             if self._timed_dataframe is not None and tof_column in self._timed_dataframe.columns:
                 tdf, _ = self.ec.apply_energy_correction(
                     df=self._timed_dataframe,
                     correction=correction,
-                    verbose=False,
+                    suppress_output=True,
                     **kwds,
                 )
 
@@ -1117,20 +1148,20 @@ def apply_energy_correction(
         else:
             raise ValueError("No dataframe loaded!")
         if preview:
-            print(self._dataframe.head(10))
+            logger.info(self._dataframe.head(10))
         else:
-            if verbose:
-                print(self._dataframe)
+            logger.info(self._dataframe)
 
     # Energy calibrator workflow
     # 1. Load and normalize data
+    @call_logger(logger)
     def load_bias_series(
         self,
-        binned_data: Union[xr.DataArray, Tuple[np.ndarray, np.ndarray, np.ndarray]] = None,
-        data_files: List[str] = None,
-        axes: List[str] = None,
-        bins: List = None,
-        ranges: Sequence[Tuple[float, float]] = None,
+        binned_data: xr.DataArray | tuple[np.ndarray, np.ndarray, np.ndarray] = None,
+        data_files: list[str] = None,
+        axes: list[str] = None,
+        bins: list = None,
+        ranges: Sequence[tuple[float, float]] = None,
         biases: np.ndarray = None,
         bias_key: str = None,
         normalize: bool = None,
@@ -1141,16 +1172,16 @@ def load_bias_series(
         single-event files, or load binned bias/TOF traces.
 
         Args:
-            binned_data (Union[xr.DataArray, Tuple[np.ndarray, np.ndarray, np.ndarray]], optional):
+            binned_data (xr.DataArray | tuple[np.ndarray, np.ndarray, np.ndarray], optional):
                 Binned data If provided as DataArray, Needs to contain dimensions
-                config["dataframe"]["tof_column"] and config["dataframe"]["bias_column"]. If
-                provided as tuple, needs to contain elements tof, biases, traces.
-            data_files (List[str], optional): list of file paths to bin
-            axes (List[str], optional): bin axes.
-                Defaults to config["dataframe"]["tof_column"].
-            bins (List, optional): number of bins.
+                config["dataframe"]["columns"]["tof"] and config["dataframe"]["columns"]["bias"].
+                If provided as tuple, needs to contain elements tof, biases, traces.
+            data_files (list[str], optional): list of file paths to bin
+            axes (list[str], optional): bin axes.
+                Defaults to config["dataframe"]["columns"]["tof"].
+            bins (list, optional): number of bins.
                 Defaults to config["energy"]["bins"].
-            ranges (Sequence[Tuple[float, float]], optional): bin ranges.
+            ranges (Sequence[tuple[float, float]], optional): bin ranges.
                 Defaults to config["energy"]["ranges"].
             biases (np.ndarray, optional): Bias voltages used. If missing, bias
                 voltages are extracted from the data files.
@@ -1168,16 +1199,16 @@ def load_bias_series(
         if binned_data is not None:
             if isinstance(binned_data, xr.DataArray):
                 if (
-                    self._config["dataframe"]["tof_column"] not in binned_data.dims
-                    or self._config["dataframe"]["bias_column"] not in binned_data.dims
+                    self._config["dataframe"]["columns"]["tof"] not in binned_data.dims
+                    or self._config["dataframe"]["columns"]["bias"] not in binned_data.dims
                 ):
                     raise ValueError(
                         "If binned_data is provided as an xarray, it needs to contain dimensions "
-                        f"'{self._config['dataframe']['tof_column']}' and "
-                        f"'{self._config['dataframe']['bias_column']}'!.",
+                        f"'{self._config['dataframe']['columns']['tof']}' and "
+                        f"'{self._config['dataframe']['columns']['bias']}'!.",
                     )
-                tof = binned_data.coords[self._config["dataframe"]["tof_column"]].values
-                biases = binned_data.coords[self._config["dataframe"]["bias_column"]].values
+                tof = binned_data.coords[self._config["dataframe"]["columns"]["tof"]].values
+                biases = binned_data.coords[self._config["dataframe"]["columns"]["bias"]].values
                 traces = binned_data.values[:, :]
             else:
                 try:
@@ -1187,17 +1218,22 @@ def load_bias_series(
                         "If binned_data is provided as tuple, it needs to contain "
                         "(tof, biases, traces)!",
                     ) from exc
+            logger.debug(f"Energy calibration data loaded from binned data. Bias values={biases}.")
             self.ec.load_data(biases=biases, traces=traces, tof=tof)
 
         elif data_files is not None:
             self.ec.bin_data(
-                data_files=cast(List[str], self.cpy(data_files)),
+                data_files=cast(list[str], self.cpy(data_files)),
                 axes=axes,
                 bins=bins,
                 ranges=ranges,
                 biases=biases,
                 bias_key=bias_key,
             )
+            logger.debug(
+                f"Energy calibration data binned from files {data_files} data. "
+                f"Bias values={biases}.",
+            )
 
         else:
             raise ValueError("Either binned_data or data_files needs to be provided!")
@@ -1213,13 +1249,17 @@ def load_bias_series(
         self.ec.view(
             traces=self.ec.traces_normed,
             xaxis=self.ec.tof,
-            backend="bokeh",
+            backend="matplotlib",
         )
+        plt.xlabel("Time-of-flight")
+        plt.ylabel("Intensity")
+        plt.tight_layout()
 
     # 2. extract ranges and get peak positions
+    @call_logger(logger)
     def find_bias_peaks(
         self,
-        ranges: Union[List[Tuple], Tuple],
+        ranges: list[tuple] | tuple,
         ref_id: int = 0,
         infer_others: bool = True,
         mode: str = "replace",
@@ -1235,7 +1275,7 @@ def find_bias_peaks(
         Alternatively, a list of ranges for all traces can be provided.
 
         Args:
-            ranges (Union[List[Tuple], Tuple]): Tuple of TOF values indicating a range.
+            ranges (list[tuple] | tuple): Tuple of TOF values indicating a range.
                 Alternatively, a list of ranges for all traces can be given.
             ref_id (int, optional): The id of the trace the range refers to.
                 Defaults to 0.
@@ -1263,18 +1303,19 @@ def find_bias_peaks(
                 mode=mode,
                 radius=radius,
             )
-            print(self.ec.featranges)
+            logger.info(f"Use feature ranges: {self.ec.featranges}.")
             try:
                 self.ec.feature_extract(peak_window=peak_window)
+                logger.info(f"Extracted energy features: {self.ec.peaks}.")
                 self.ec.view(
                     traces=self.ec.traces_normed,
                     segs=self.ec.featranges,
                     xaxis=self.ec.tof,
                     peaks=self.ec.peaks,
-                    backend="bokeh",
+                    backend="matplotlib",
                 )
             except IndexError:
-                print("Could not determine all peaks!")
+                logger.error("Could not determine all peaks!")
                 raise
         else:
             # New adjustment tool
@@ -1290,13 +1331,12 @@ def find_bias_peaks(
             )
 
     # 3. Fit the energy calibration relation
+    @call_logger(logger)
     def calibrate_energy_axis(
         self,
-        ref_id: int,
         ref_energy: float,
         method: str = None,
         energy_scale: str = None,
-        verbose: bool = None,
         **kwds,
     ):
         """3. Step of the energy calibration workflow: Calculate the calibration
@@ -1305,10 +1345,7 @@ def calibrate_energy_axis(
         approximation, and a d^2/(t-t0)^2 relation.
 
         Args:
-            ref_id (int): id of the trace at the bias where the reference energy is
-                given.
-            ref_energy (float): Absolute energy of the detected feature at the bias
-                of ref_id
+            ref_energy (float): Binding/kinetic energy of the detected feature.
             method (str, optional): Method for determining the energy calibration.
 
                 - **'lmfit'**: Energy calibration using lmfit and 1/t^2 form.
@@ -1321,13 +1358,8 @@ def calibrate_energy_axis(
                 - **'binding'**: increasing energy with increasing TOF.
 
                 Defaults to config["energy"]["energy_scale"]
-            verbose (bool, optional): Option to print out diagnostic information.
-                Defaults to config["core"]["verbose"].
             **kwds**: Keyword parameters passed to ``EnergyCalibrator.calibrate()``.
         """
-        if verbose is None:
-            verbose = self.verbose
-
         if method is None:
             method = self._config["energy"]["calibration_method"]
 
@@ -1335,41 +1367,51 @@ def calibrate_energy_axis(
             energy_scale = self._config["energy"]["energy_scale"]
 
         self.ec.calibrate(
-            ref_id=ref_id,
             ref_energy=ref_energy,
             method=method,
             energy_scale=energy_scale,
-            verbose=verbose,
             **kwds,
         )
-        if verbose:
+        if self._verbose:
             if self.ec.traces_normed is not None:
-                print("Quality of Calibration:")
                 self.ec.view(
                     traces=self.ec.traces_normed,
                     xaxis=self.ec.calibration["axis"],
                     align=True,
                     energy_scale=energy_scale,
-                    backend="bokeh",
+                    backend="matplotlib",
+                    title="Quality of Calibration",
                 )
-            print("E/TOF relationship:")
-            self.ec.view(
-                traces=self.ec.calibration["axis"][None, :],
-                xaxis=self.ec.tof,
-                backend="matplotlib",
-                show_legend=False,
-            )
+                plt.xlabel("Energy (eV)")
+                plt.ylabel("Intensity")
+                plt.tight_layout()
+                plt.show()
             if energy_scale == "kinetic":
+                self.ec.view(
+                    traces=self.ec.calibration["axis"][None, :] + self.ec.biases[0],
+                    xaxis=self.ec.tof,
+                    backend="matplotlib",
+                    show_legend=False,
+                    title="E/TOF relationship",
+                )
                 plt.scatter(
                     self.ec.peaks[:, 0],
-                    -(self.ec.biases - self.ec.biases[ref_id]) + ref_energy,
+                    -(self.ec.biases - self.ec.biases[0]) + ref_energy,
                     s=50,
                     c="k",
                 )
+                plt.tight_layout()
             elif energy_scale == "binding":
+                self.ec.view(
+                    traces=self.ec.calibration["axis"][None, :] - self.ec.biases[0],
+                    xaxis=self.ec.tof,
+                    backend="matplotlib",
+                    show_legend=False,
+                    title="E/TOF relationship",
+                )
                 plt.scatter(
                     self.ec.peaks[:, 0],
-                    self.ec.biases - self.ec.biases[ref_id] + ref_energy,
+                    self.ec.biases - self.ec.biases[0] + ref_energy,
                     s=50,
                     c="k",
                 )
@@ -1378,8 +1420,9 @@ def calibrate_energy_axis(
                     'energy_scale needs to be either "binding" or "kinetic"',
                     f", got {energy_scale}.",
                 )
-            plt.xlabel("Time-of-flight", fontsize=15)
-            plt.ylabel("Energy (eV)", fontsize=15)
+            plt.xlabel("Time-of-flight")
+            plt.ylabel("Energy (eV)")
+            plt.tight_layout()
             plt.show()
 
     # 3a. Save energy calibration parameters to config file.
@@ -1408,22 +1451,25 @@ def save_energy_calibration(
                 calibration[key] = value
             elif key == "coeffs":
                 calibration[key] = [float(i) for i in value]
+            elif key == "creation_date":
+                calibration[key] = value.isoformat()
             else:
                 calibration[key] = float(value)
 
         if "creation_date" not in calibration:
-            calibration["creation_date"] = datetime.now().timestamp()
+            calibration["creation_date"] = datetime.now().isoformat()
 
         config = {"energy": {"calibration": calibration}}
         save_config(config, filename, overwrite)
-        print(f'Saved energy calibration parameters to "{filename}".')
+        logger.info(f'Saved energy calibration parameters to "{filename}".')
 
     # 4. Apply energy calibration to the dataframe
+    @call_logger(logger)
     def append_energy_axis(
         self,
         calibration: dict = None,
+        bias_voltage: float = None,
         preview: bool = False,
-        verbose: bool = None,
         **kwds,
     ):
         """4. step of the energy calibration workflow: Apply the calibration function
@@ -1435,31 +1481,29 @@ def append_energy_axis(
             calibration (dict, optional): Calibration dict containing calibration
                 parameters. Overrides calibration from class or config.
                 Defaults to None.
+            bias_voltage (float, optional): Sample bias voltage of the scan data. If omitted,
+                the bias voltage is being read from the dataframe. If it is not found there,
+                a warning is printed and the calibrated data might have an offset.
             preview (bool): Option to preview the first elements of the data frame.
-            verbose (bool, optional): Option to print out diagnostic information.
-                Defaults to config["core"]["verbose"].
             **kwds:
                 Keyword args passed to ``EnergyCalibrator.append_energy_axis()``.
         """
-        if verbose is None:
-            verbose = self.verbose
-
-        tof_column = self._config["dataframe"]["tof_column"]
+        tof_column = self._config["dataframe"]["columns"]["tof"]
 
         if self._dataframe is not None:
-            if verbose:
-                print("Adding energy column to dataframe:")
+            logger.info("Adding energy column to dataframe:")
             df, metadata = self.ec.append_energy_axis(
                 df=self._dataframe,
                 calibration=calibration,
-                verbose=verbose,
+                bias_voltage=bias_voltage,
                 **kwds,
             )
             if self._timed_dataframe is not None and tof_column in self._timed_dataframe.columns:
                 tdf, _ = self.ec.append_energy_axis(
                     df=self._timed_dataframe,
                     calibration=calibration,
-                    verbose=False,
+                    bias_voltage=bias_voltage,
+                    suppress_output=True,
                     **kwds,
                 )
 
@@ -1476,54 +1520,48 @@ def append_energy_axis(
         else:
             raise ValueError("No dataframe loaded!")
         if preview:
-            print(self._dataframe.head(10))
+            logger.info(self._dataframe.head(10))
         else:
-            if verbose:
-                print(self._dataframe)
+            logger.info(self._dataframe)
 
+    @call_logger(logger)
     def add_energy_offset(
         self,
         constant: float = None,
-        columns: Union[str, Sequence[str]] = None,
-        weights: Union[float, Sequence[float]] = None,
-        reductions: Union[str, Sequence[str]] = None,
-        preserve_mean: Union[bool, Sequence[bool]] = None,
+        columns: str | Sequence[str] = None,
+        weights: float | Sequence[float] = None,
+        reductions: str | Sequence[str] = None,
+        preserve_mean: bool | Sequence[bool] = None,
         preview: bool = False,
-        verbose: bool = None,
     ) -> None:
         """Shift the energy axis of the dataframe by a given amount.
 
         Args:
             constant (float, optional): The constant to shift the energy axis by.
-            columns (Union[str, Sequence[str]]): Name of the column(s) to apply the shift from.
-            weights (Union[float, Sequence[float]]): weights to apply to the columns.
+            columns (str | Sequence[str], optional): Name of the column(s) to apply the shift from.
+            weights (float | Sequence[float], optional): weights to apply to the columns.
                 Can also be used to flip the sign (e.g. -1). Defaults to 1.
-            preserve_mean (bool): Whether to subtract the mean of the column before applying the
-                shift. Defaults to False.
-            reductions (str): The reduction to apply to the column. Should be an available method
-                of dask.dataframe.Series. For example "mean". In this case the function is applied
-                to the column to generate a single value for the whole dataset. If None, the shift
-                is applied per-dataframe-row. Defaults to None. Currently only "mean" is supported.
+            reductions (str | Sequence[str], optional): The reduction to apply to the column.
+                Should be an available method of dask.dataframe.Series. For example "mean". In this
+                case the function is applied to the column to generate a single value for the whole
+                dataset. If None, the shift is applied per-dataframe-row. Defaults to None.
+                Currently only "mean" is supported.
+            preserve_mean (bool | Sequence[bool], optional): Whether to subtract the mean of the
+                column before applying the shift. Defaults to False.
             preview (bool, optional): Option to preview the first elements of the data frame.
                 Defaults to False.
-            verbose (bool, optional): Option to print out diagnostic information.
-                Defaults to config["core"]["verbose"].
 
         Raises:
             ValueError: If the energy column is not in the dataframe.
         """
-        if verbose is None:
-            verbose = self.verbose
-
-        energy_column = self._config["dataframe"]["energy_column"]
+        energy_column = self._config["dataframe"]["columns"]["energy"]
         if energy_column not in self._dataframe.columns:
             raise ValueError(
                 f"Energy column {energy_column} not found in dataframe! "
                 "Run `append_energy_axis()` first.",
             )
         if self.dataframe is not None:
-            if verbose:
-                print("Adding energy offset to dataframe:")
+            logger.info("Adding energy offset to dataframe:")
             df, metadata = self.ec.add_offsets(
                 df=self._dataframe,
                 constant=constant,
@@ -1532,7 +1570,6 @@ def add_energy_offset(
                 weights=weights,
                 reductions=reductions,
                 preserve_mean=preserve_mean,
-                verbose=verbose,
             )
             if self._timed_dataframe is not None and energy_column in self._timed_dataframe.columns:
                 tdf, _ = self.ec.add_offsets(
@@ -1543,6 +1580,7 @@ def add_energy_offset(
                     weights=weights,
                     reductions=reductions,
                     preserve_mean=preserve_mean,
+                    suppress_output=True,
                 )
 
             self._attributes.add(
@@ -1558,9 +1596,9 @@ def add_energy_offset(
         else:
             raise ValueError("No dataframe loaded!")
         if preview:
-            print(self._dataframe.head(10))
-        elif verbose:
-            print(self._dataframe)
+            logger.info(self._dataframe.head(10))
+        else:
+            logger.info(self._dataframe)
 
     def save_energy_offset(
         self,
@@ -1580,17 +1618,21 @@ def save_energy_offset(
         if len(self.ec.offsets) == 0:
             raise ValueError("No energy offset parameters to save!")
 
-        if "creation_date" not in self.ec.offsets.keys():
-            self.ec.offsets["creation_date"] = datetime.now().timestamp()
+        offsets = deepcopy(self.ec.offsets)
+
+        if "creation_date" not in offsets.keys():
+            offsets["creation_date"] = datetime.now()
 
-        config = {"energy": {"offsets": self.ec.offsets}}
+        offsets["creation_date"] = offsets["creation_date"].isoformat()
+
+        config = {"energy": {"offsets": offsets}}
         save_config(config, filename, overwrite)
-        print(f'Saved energy offset parameters to "{filename}".')
+        logger.info(f'Saved energy offset parameters to "{filename}".')
 
+    @call_logger(logger)
     def append_tof_ns_axis(
         self,
         preview: bool = False,
-        verbose: bool = None,
         **kwds,
     ):
         """Convert time-of-flight channel steps to nanoseconds.
@@ -1598,22 +1640,16 @@ def append_tof_ns_axis(
         Args:
             tof_ns_column (str, optional): Name of the generated column containing the
                 time-of-flight in nanosecond.
-                Defaults to config["dataframe"]["tof_ns_column"].
+                Defaults to config["dataframe"]["columns"]["tof_ns"].
             preview (bool, optional): Option to preview the first elements of the data frame.
                 Defaults to False.
-            verbose (bool, optional): Option to print out diagnostic information.
-                Defaults to config["core"]["verbose"].
-            **kwds: additional arguments are passed to ``EnergyCalibrator.tof_step_to_ns()``.
+            **kwds: additional arguments are passed to ``EnergyCalibrator.append_tof_ns_axis()``.
 
         """
-        if verbose is None:
-            verbose = self.verbose
-
-        tof_column = self._config["dataframe"]["tof_column"]
+        tof_column = self._config["dataframe"]["columns"]["tof"]
 
         if self._dataframe is not None:
-            if verbose:
-                print("Adding time-of-flight column in nanoseconds to dataframe:")
+            logger.info("Adding time-of-flight column in nanoseconds to dataframe.")
             # TODO assert order of execution through metadata
 
             df, metadata = self.ec.append_tof_ns_axis(
@@ -1637,16 +1673,15 @@ def append_tof_ns_axis(
         else:
             raise ValueError("No dataframe loaded!")
         if preview:
-            print(self._dataframe.head(10))
+            logger.info(self._dataframe.head(10))
         else:
-            if verbose:
-                print(self._dataframe)
+            logger.info(self._dataframe)
 
+    @call_logger(logger)
     def align_dld_sectors(
         self,
         sector_delays: np.ndarray = None,
         preview: bool = False,
-        verbose: bool = None,
         **kwds,
     ):
         """Align the 8s sectors of the HEXTOF endstation.
@@ -1656,18 +1691,12 @@ def align_dld_sectors(
                 config["dataframe"]["sector_delays"].
             preview (bool, optional): Option to preview the first elements of the data frame.
                 Defaults to False.
-            verbose (bool, optional): Option to print out diagnostic information.
-                Defaults to config["core"]["verbose"].
             **kwds: additional arguments are passed to ``EnergyCalibrator.align_dld_sectors()``.
         """
-        if verbose is None:
-            verbose = self.verbose
-
-        tof_column = self._config["dataframe"]["tof_column"]
+        tof_column = self._config["dataframe"]["columns"]["tof"]
 
         if self._dataframe is not None:
-            if verbose:
-                print("Aligning 8s sectors of dataframe")
+            logger.info("Aligning 8s sectors of dataframe")
             # TODO assert order of execution through metadata
 
             df, metadata = self.ec.align_dld_sectors(
@@ -1693,26 +1722,25 @@ def align_dld_sectors(
         else:
             raise ValueError("No dataframe loaded!")
         if preview:
-            print(self._dataframe.head(10))
+            logger.info(self._dataframe.head(10))
         else:
-            if verbose:
-                print(self._dataframe)
+            logger.info(self._dataframe)
 
     # Delay calibration function
+    @call_logger(logger)
     def calibrate_delay_axis(
         self,
-        delay_range: Tuple[float, float] = None,
+        delay_range: tuple[float, float] = None,
         read_delay_ranges: bool = True,
         datafile: str = None,
         preview: bool = False,
-        verbose: bool = None,
         **kwds,
     ):
         """Append delay column to dataframe. Either provide delay ranges, or read
         them from a file.
 
         Args:
-            delay_range (Tuple[float, float], optional): The scanned delay range in
+            delay_range (tuple[float, float], optional): The scanned delay range in
                 picoseconds. Defaults to None.
             read_delay_ranges (bool, optional): Option whether to read the delay ranges from the
                 data. Defaults to True. If false, parameters in the config will be used.
@@ -1720,34 +1748,27 @@ def calibrate_delay_axis(
                 Defaults to the first file of the dataset.
             preview (bool, optional): Option to preview the first elements of the data frame.
                 Defaults to False.
-            verbose (bool, optional): Option to print out diagnostic information.
-                Defaults to config["core"]["verbose"].
             **kwds: Keyword args passed to ``DelayCalibrator.append_delay_axis``.
         """
-        if verbose is None:
-            verbose = self.verbose
-
-        adc_column = self._config["dataframe"]["adc_column"]
+        adc_column = self._config["dataframe"]["columns"]["adc"]
         if adc_column not in self._dataframe.columns:
             raise ValueError(f"ADC column {adc_column} not found in dataframe, cannot calibrate!")
 
         if self._dataframe is not None:
-            if verbose:
-                print("Adding delay column to dataframe:")
+            logger.info("Adding delay column to dataframe:")
 
-            if read_delay_ranges and delay_range is None:
+            if read_delay_ranges and delay_range is None and datafile is None:
                 try:
                     datafile = self._files[0]
-                except IndexError:
+                except IndexError as exc:
                     raise ValueError(
                         "No datafile available, specify either 'datafile' or 'delay_range'.",
-                    )
+                    ) from exc
 
             df, metadata = self.dc.append_delay_axis(
                 self._dataframe,
                 delay_range=delay_range,
                 datafile=datafile,
-                verbose=verbose,
                 **kwds,
             )
             if self._timed_dataframe is not None and adc_column in self._timed_dataframe.columns:
@@ -1755,7 +1776,7 @@ def calibrate_delay_axis(
                     self._timed_dataframe,
                     delay_range=delay_range,
                     datafile=datafile,
-                    verbose=False,
+                    suppress_output=True,
                     **kwds,
                 )
 
@@ -1771,10 +1792,9 @@ def calibrate_delay_axis(
         else:
             raise ValueError("No dataframe loaded!")
         if preview:
-            print(self._dataframe.head(10))
+            logger.info(self._dataframe.head(10))
         else:
-            if self.verbose:
-                print(self._dataframe)
+            logger.debug(self._dataframe)
 
     def save_delay_calibration(
         self,
@@ -1800,11 +1820,13 @@ def save_delay_calibration(
                 calibration[key] = value
             elif key in ["adc_range", "delay_range", "delay_range_mm"]:
                 calibration[key] = [float(i) for i in value]
+            elif key == "creation_date":
+                calibration[key] = value.isoformat()
             else:
                 calibration[key] = float(value)
 
         if "creation_date" not in calibration:
-            calibration["creation_date"] = datetime.now().timestamp()
+            calibration["creation_date"] = datetime.now().isoformat()
 
         config = {
             "delay": {
@@ -1813,49 +1835,44 @@ def save_delay_calibration(
         }
         save_config(config, filename, overwrite)
 
+    @call_logger(logger)
     def add_delay_offset(
         self,
         constant: float = None,
         flip_delay_axis: bool = None,
-        columns: Union[str, Sequence[str]] = None,
-        weights: Union[float, Sequence[float]] = 1.0,
-        reductions: Union[str, Sequence[str]] = None,
-        preserve_mean: Union[bool, Sequence[bool]] = False,
+        columns: str | Sequence[str] = None,
+        weights: float | Sequence[float] = 1.0,
+        reductions: str | Sequence[str] = None,
+        preserve_mean: bool | Sequence[bool] = False,
         preview: bool = False,
-        verbose: bool = None,
     ) -> None:
         """Shift the delay axis of the dataframe by a constant or other columns.
 
         Args:
             constant (float, optional): The constant to shift the delay axis by.
             flip_delay_axis (bool, optional): Option to reverse the direction of the delay axis.
-            columns (Union[str, Sequence[str]]): Name of the column(s) to apply the shift from.
-            weights (Union[float, Sequence[float]]): weights to apply to the columns.
+            columns (str | Sequence[str], optional): Name of the column(s) to apply the shift from.
+            weights (float | Sequence[float], optional): weights to apply to the columns.
                 Can also be used to flip the sign (e.g. -1). Defaults to 1.
-            preserve_mean (bool): Whether to subtract the mean of the column before applying the
-                shift. Defaults to False.
-            reductions (str): The reduction to apply to the column. Should be an available method
-                of dask.dataframe.Series. For example "mean". In this case the function is applied
-                to the column to generate a single value for the whole dataset. If None, the shift
-                is applied per-dataframe-row. Defaults to None. Currently only "mean" is supported.
+            reductions (str | Sequence[str], optional): The reduction to apply to the column.
+                Should be an available method of dask.dataframe.Series. For example "mean". In this
+                case the function is applied to the column to generate a single value for the whole
+                dataset. If None, the shift is applied per-dataframe-row. Defaults to None.
+                Currently only "mean" is supported.
+            preserve_mean (bool | Sequence[bool], optional): Whether to subtract the mean of the
+                column before applying the shift. Defaults to False.
             preview (bool, optional): Option to preview the first elements of the data frame.
                 Defaults to False.
-            verbose (bool, optional): Option to print out diagnostic information.
-                Defaults to config["core"]["verbose"].
 
         Raises:
             ValueError: If the delay column is not in the dataframe.
         """
-        if verbose is None:
-            verbose = self.verbose
-
-        delay_column = self._config["dataframe"]["delay_column"]
+        delay_column = self._config["dataframe"]["columns"]["delay"]
         if delay_column not in self._dataframe.columns:
             raise ValueError(f"Delay column {delay_column} not found in dataframe! ")
 
         if self.dataframe is not None:
-            if verbose:
-                print("Adding delay offset to dataframe:")
+            logger.info("Adding delay offset to dataframe:")
             df, metadata = self.dc.add_offsets(
                 df=self._dataframe,
                 constant=constant,
@@ -1865,7 +1882,6 @@ def add_delay_offset(
                 weights=weights,
                 reductions=reductions,
                 preserve_mean=preserve_mean,
-                verbose=verbose,
             )
             if self._timed_dataframe is not None and delay_column in self._timed_dataframe.columns:
                 tdf, _ = self.dc.add_offsets(
@@ -1877,7 +1893,7 @@ def add_delay_offset(
                     weights=weights,
                     reductions=reductions,
                     preserve_mean=preserve_mean,
-                    verbose=False,
+                    suppress_output=True,
                 )
 
             self._attributes.add(
@@ -1891,10 +1907,9 @@ def add_delay_offset(
         else:
             raise ValueError("No dataframe loaded!")
         if preview:
-            print(self._dataframe.head(10))
+            logger.info(self._dataframe.head(10))
         else:
-            if verbose:
-                print(self._dataframe)
+            logger.info(self._dataframe)
 
     def save_delay_offsets(
         self,
@@ -1914,16 +1929,16 @@ def save_delay_offsets(
         if len(self.dc.offsets) == 0:
             raise ValueError("No delay offset parameters to save!")
 
-        if "creation_date" not in self.ec.offsets.keys():
-            self.ec.offsets["creation_date"] = datetime.now().timestamp()
+        offsets = deepcopy(self.dc.offsets)
 
-        config = {
-            "delay": {
-                "offsets": self.dc.offsets,
-            },
-        }
+        if "creation_date" not in offsets.keys():
+            offsets["creation_date"] = datetime.now()
+
+        offsets["creation_date"] = offsets["creation_date"].isoformat()
+
+        config = {"delay": {"offsets": offsets}}
         save_config(config, filename, overwrite)
-        print(f'Saved delay offset parameters to "{filename}".')
+        logger.info(f'Saved delay offset parameters to "{filename}".')
 
     def save_workflow_params(
         self,
@@ -1953,18 +1968,19 @@ def save_workflow_params(
             except (ValueError, AttributeError, KeyError):
                 pass
 
+    @call_logger(logger)
     def add_jitter(
         self,
-        cols: List[str] = None,
-        amps: Union[float, Sequence[float]] = None,
+        cols: list[str] = None,
+        amps: float | Sequence[float] = None,
         **kwds,
     ):
         """Add jitter to the selected dataframe columns.
 
         Args:
-            cols (List[str], optional): The columns onto which to apply jitter.
+            cols (list[str], optional): The columns onto which to apply jitter.
                 Defaults to config["dataframe"]["jitter_cols"].
-            amps (Union[float, Sequence[float]], optional): Amplitude scalings for the
+            amps (float | Sequence[float], optional): Amplitude scalings for the
                 jittering noise. If one number is given, the same is used for all axes.
                 For uniform noise (default) it will cover the interval [-amp, +amp].
                 Defaults to config["dataframe"]["jitter_amps"].
@@ -1974,7 +1990,7 @@ def add_jitter(
             cols = self._config["dataframe"]["jitter_cols"]
         for loc, col in enumerate(cols):
             if col.startswith("@"):
-                cols[loc] = self._config["dataframe"].get(col.strip("@"))
+                cols[loc] = self._config["dataframe"]["columns"].get(col.strip("@"))
 
         if amps is None:
             amps = self._config["dataframe"]["jitter_amps"]
@@ -2003,7 +2019,9 @@ def add_jitter(
             metadata.append(col)
         # TODO: allow only appending if columns are not jittered yet
         self._attributes.add(metadata, "jittering", duplicate_policy="append")
+        logger.info(f"add_jitter: Added jitter to columns {cols}.")
 
+    @call_logger(logger)
     def add_time_stamped_data(
         self,
         dest_column: str,
@@ -2024,11 +2042,15 @@ def add_time_stamped_data(
                 If omitted, data are retrieved from the epics archiver.
             archiver_channel (str, optional): EPICS archiver channel from which to retrieve data.
                 Either this or data and time_stamps have to be present.
-            **kwds: additional keyword arguments passed to ``add_time_stamped_data``.
+            **kwds:
+
+                - **time_stamp_column**: Dataframe column containing time-stamp data
+
+                Additional keyword arguments passed to ``add_time_stamped_data``.
         """
         time_stamp_column = kwds.pop(
             "time_stamp_column",
-            self._config["dataframe"].get("time_stamp_alias", ""),
+            self._config["dataframe"]["columns"].get("timestamp", ""),
         )
 
         if time_stamps is None and data is None:
@@ -2067,30 +2089,32 @@ def add_time_stamped_data(
                     time_stamp_column=time_stamp_column,
                     **kwds,
                 )
-        metadata: List[Any] = []
+        metadata: list[Any] = []
         metadata.append(dest_column)
         metadata.append(time_stamps)
         metadata.append(data)
         self._attributes.add(metadata, "time_stamped_data", duplicate_policy="append")
+        logger.info(f"add_time_stamped_data: Added time-stamped data as column {dest_column}.")
 
+    @call_logger(logger)
     def pre_binning(
         self,
-        df_partitions: Union[int, Sequence[int]] = 100,
-        axes: List[str] = None,
-        bins: List[int] = None,
-        ranges: Sequence[Tuple[float, float]] = None,
+        df_partitions: int | Sequence[int] = 100,
+        axes: list[str] = None,
+        bins: list[int] = None,
+        ranges: Sequence[tuple[float, float]] = None,
         **kwds,
     ) -> xr.DataArray:
         """Function to do an initial binning of the dataframe loaded to the class.
 
         Args:
-            df_partitions (Union[int, Sequence[int]], optional): Number of dataframe partitions to
+            df_partitions (int | Sequence[int], optional): Number of dataframe partitions to
                 use for the initial binning. Defaults to 100.
-            axes (List[str], optional): Axes to bin.
+            axes (list[str], optional): Axes to bin.
                 Defaults to config["momentum"]["axes"].
-            bins (List[int], optional): Bin numbers to use for binning.
+            bins (list[int], optional): Bin numbers to use for binning.
                 Defaults to config["momentum"]["bins"].
-            ranges (List[Tuple], optional): Ranges to use for binning.
+            ranges (Sequence[tuple[float, float]], optional): Ranges to use for binning.
                 Defaults to config["momentum"]["ranges"].
             **kwds: Keyword argument passed to ``compute``.
 
@@ -2101,16 +2125,14 @@ def pre_binning(
             axes = self._config["momentum"]["axes"]
         for loc, axis in enumerate(axes):
             if axis.startswith("@"):
-                axes[loc] = self._config["dataframe"].get(axis.strip("@"))
+                axes[loc] = self._config["dataframe"]["columns"].get(axis.strip("@"))
 
         if bins is None:
             bins = self._config["momentum"]["bins"]
         if ranges is None:
             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_]
+            ranges_[2] = np.asarray(ranges_[2]) / self._config["dataframe"]["tof_binning"]
+            ranges = [cast(tuple[float, float], tuple(v)) for v in ranges_]
 
         assert self._dataframe is not None, "dataframe needs to be loaded first!"
 
@@ -2122,25 +2144,19 @@ def pre_binning(
             **kwds,
         )
 
+    @call_logger(logger)
     def compute(
         self,
-        bins: Union[
-            int,
-            dict,
-            tuple,
-            List[int],
-            List[np.ndarray],
-            List[tuple],
-        ] = 100,
-        axes: Union[str, Sequence[str]] = None,
-        ranges: Sequence[Tuple[float, float]] = None,
-        normalize_to_acquisition_time: Union[bool, str] = False,
+        bins: int | dict | tuple | list[int] | list[np.ndarray] | list[tuple] = 100,
+        axes: str | Sequence[str] = None,
+        ranges: Sequence[tuple[float, float]] = None,
+        normalize_to_acquisition_time: bool | str = False,
         **kwds,
     ) -> xr.DataArray:
         """Compute the histogram along the given dimensions.
 
         Args:
-            bins (int, dict, tuple, List[int], List[np.ndarray], List[tuple], optional):
+            bins (int | dict | tuple | list[int] | list[np.ndarray] | list[tuple], optional):
                 Definition of the bins. Can be any of the following cases:
 
                 - an integer describing the number of bins in on all dimensions
@@ -2151,12 +2167,12 @@ def compute(
                 - a dictionary made of the axes as keys and any of the above as values.
 
                 This takes priority over the axes and range arguments. Defaults to 100.
-            axes (Union[str, Sequence[str]], optional): The names of the axes (columns)
+            axes (str | Sequence[str], optional): The names of the axes (columns)
                 on which to calculate the histogram. The order will be the order of the
                 dimensions in the resulting array. Defaults to None.
-            ranges (Sequence[Tuple[float, float]], optional): list of tuples containing
+            ranges (Sequence[tuple[float, float]], optional): list of tuples containing
                 the start and end point of the binning range. Defaults to None.
-            normalize_to_acquisition_time (Union[bool, str]): Option to normalize the
+            normalize_to_acquisition_time (bool | str): Option to normalize the
                 result to the acquisition time. If a "slow" axis was scanned, providing
                 the name of the scanned axis will compute and apply the corresponding
                 normalization histogram. Defaults to False.
@@ -2171,7 +2187,7 @@ def compute(
                 - **pbar**: Option to show the tqdm progress bar. Defaults to
                   config["binning"]["pbar"].
                 - **n_cores**: Number of CPU cores to use for parallelization.
-                  Defaults to config["binning"]["num_cores"] or N_CPU-1.
+                  Defaults to config["core"]["num_cores"] or N_CPU-1.
                 - **threads_per_worker**: Limit the number of threads that
                   multiprocessing can spawn per binning thread. Defaults to
                   config["binning"]["threads_per_worker"].
@@ -2198,7 +2214,7 @@ def compute(
         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"])
+        num_cores = kwds.pop("num_cores", self._config["core"]["num_cores"])
         threads_per_worker = kwds.pop(
             "threads_per_worker",
             self._config["binning"]["threads_per_worker"],
@@ -2207,7 +2223,7 @@ def compute(
             "threadpool_API",
             self._config["binning"]["threadpool_API"],
         )
-        df_partitions: Union[int, Sequence[int]] = kwds.pop("df_partitions", None)
+        df_partitions: int | Sequence[int] = kwds.pop("df_partitions", None)
         if isinstance(df_partitions, int):
             df_partitions = list(range(0, min(df_partitions, self._dataframe.npartitions)))
         if df_partitions is not None:
@@ -2260,9 +2276,7 @@ def compute(
         if normalize_to_acquisition_time:
             if isinstance(normalize_to_acquisition_time, str):
                 axis = normalize_to_acquisition_time
-                print(
-                    f"Calculate normalization histogram for axis '{axis}'...",
-                )
+                logger.info(f"Calculate normalization histogram for axis '{axis}'...")
                 self._normalization_histogram = self.get_normalization_histogram(
                     axis=axis,
                     df_partitions=df_partitions,
@@ -2294,6 +2308,7 @@ def compute(
 
         return self._binned
 
+    @call_logger(logger)
     def get_normalization_histogram(
         self,
         axis: str = "delay",
@@ -2329,7 +2344,13 @@ def get_normalization_histogram(
         if axis not in self._binned.coords:
             raise ValueError(f"Axis '{axis}' not found in binned data!")
 
-        df_partitions: Union[int, Sequence[int]] = kwds.pop("df_partitions", None)
+        df_partitions: int | Sequence[int] = kwds.pop("df_partitions", None)
+
+        if len(kwds) > 0:
+            raise TypeError(
+                f"get_normalization_histogram() got unexpected keyword arguments {kwds.keys()}.",
+            )
+
         if isinstance(df_partitions, int):
             df_partitions = list(range(0, min(df_partitions, self._dataframe.npartitions)))
         if use_time_stamps or self._timed_dataframe is None:
@@ -2338,14 +2359,14 @@ def get_normalization_histogram(
                     self._dataframe.partitions[df_partitions],
                     axis,
                     self._binned.coords[axis].values,
-                    self._config["dataframe"]["time_stamp_alias"],
+                    self._config["dataframe"]["columns"]["timestamp"],
                 )
             else:
                 self._normalization_histogram = normalization_histogram_from_timestamps(
                     self._dataframe,
                     axis,
                     self._binned.coords[axis].values,
-                    self._config["dataframe"]["time_stamp_alias"],
+                    self._config["dataframe"]["columns"]["timestamp"],
                 )
         else:
             if df_partitions is not None:
@@ -2371,8 +2392,8 @@ def view_event_histogram(
         ncol: int = 2,
         bins: Sequence[int] = None,
         axes: Sequence[str] = None,
-        ranges: Sequence[Tuple[float, float]] = None,
-        backend: str = "bokeh",
+        ranges: Sequence[tuple[float, float]] = None,
+        backend: str = "matplotlib",
         legend: bool = True,
         histkwds: dict = None,
         legkwds: dict = None,
@@ -2388,10 +2409,10 @@ def view_event_histogram(
                 axes. Defaults to config["histogram"]["bins"].
             axes (Sequence[str], optional): Names of the axes to display.
                 Defaults to config["histogram"]["axes"].
-            ranges (Sequence[Tuple[float, float]], optional): Value ranges of all
+            ranges (Sequence[tuple[float, float]], optional): Value ranges of all
                 specified axes. Defaults to config["histogram"]["ranges"].
             backend (str, optional): Backend of the plotting library
-                ('matplotlib' or 'bokeh'). Defaults to "bokeh".
+                ("matplotlib" or "bokeh"). Defaults to "matplotlib".
             legend (bool, optional): Option to include a legend in the histogram plots.
                 Defaults to True.
             histkwds (dict, optional): Keyword arguments for histograms
@@ -2411,18 +2432,14 @@ def view_event_histogram(
         axes = list(axes)
         for loc, axis in enumerate(axes):
             if axis.startswith("@"):
-                axes[loc] = self._config["dataframe"].get(axis.strip("@"))
+                axes[loc] = self._config["dataframe"]["columns"].get(axis.strip("@"))
         if ranges is None:
             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
-                    )
+                if axis == self._config["dataframe"]["columns"]["tof"]:
+                    ranges[loc] = np.asarray(ranges[loc]) / self._config["dataframe"]["tof_binning"]
+                elif axis == self._config["dataframe"]["columns"]["adc"]:
+                    ranges[loc] = np.asarray(ranges[loc]) / self._config["dataframe"]["adc_binning"]
 
         input_types = map(type, [axes, bins, ranges])
         allowed_types = [list, tuple]
@@ -2456,6 +2473,7 @@ def view_event_histogram(
             **kwds,
         )
 
+    @call_logger(logger)
     def save(
         self,
         faddr: str,
@@ -2523,7 +2541,7 @@ def save(
                 )
                 input_files = kwds.pop(
                     "input_files",
-                    self._config["nexus"]["input_files"],
+                    [str(path) for path in self._config["nexus"]["input_files"]],
                 )
             except KeyError as exc:
                 raise ValueError(
diff --git a/sed/dataset/__init__.py b/src/sed/dataset/__init__.py
similarity index 100%
rename from sed/dataset/__init__.py
rename to src/sed/dataset/__init__.py
diff --git a/sed/dataset/dataset.py b/src/sed/dataset/dataset.py
similarity index 97%
rename from sed/dataset/dataset.py
rename to src/sed/dataset/dataset.py
index 9fb4a3d3..eda5e92a 100644
--- a/sed/dataset/dataset.py
+++ b/src/sed/dataset/dataset.py
@@ -10,6 +10,7 @@
 import shutil
 import zipfile
 from datetime import datetime
+from importlib.util import find_spec
 
 import requests
 from tqdm.auto import tqdm
@@ -20,9 +21,10 @@
 from sed.core.config import USER_CONFIG_PATH
 from sed.core.logging import setup_logging
 
+package_dir = os.path.dirname(find_spec("sed").origin)
 
 # Configure logging
-logger = setup_logging(__name__)
+logger = setup_logging("dataset")
 
 
 class DatasetsManager:
@@ -32,7 +34,7 @@ class DatasetsManager:
     FILENAME = NAME + ".json"
     json_path = {}
     json_path["user"] = os.path.join(USER_CONFIG_PATH, FILENAME)
-    json_path["module"] = os.path.join(os.path.dirname(__file__), FILENAME)
+    json_path["module"] = os.path.join(package_dir, "config", FILENAME)
     json_path["folder"] = "./" + FILENAME
 
     @staticmethod
@@ -51,9 +53,11 @@ def load_datasets_dict() -> dict:
 
         return parse_config(
             folder_config=DatasetsManager.json_path["folder"],
-            system_config=DatasetsManager.json_path["user"],
+            user_config=DatasetsManager.json_path["user"],
+            system_config={},
             default_config=DatasetsManager.json_path["module"],
             verbose=False,
+            verify_config=False,
         )
 
     @staticmethod
diff --git a/sed/diagnostics.py b/src/sed/diagnostics.py
similarity index 74%
rename from sed/diagnostics.py
rename to src/sed/diagnostics.py
index 46cbc368..4f44b1f7 100644
--- a/sed/diagnostics.py
+++ b/src/sed/diagnostics.py
@@ -1,8 +1,9 @@
 """This module contains diagnostic output functions for the sed module
 
 """
-from typing import Sequence
-from typing import Tuple
+from __future__ import annotations
+
+from collections.abc import Sequence
 
 import bokeh.plotting as pbk
 import matplotlib.pyplot as plt
@@ -23,7 +24,10 @@ def plot_single_hist(
         histvals (np.ndarray): Histogram counts (e.g. vertical axis).
         edges (np.ndarray): Histogram edge values (e.g. horizontal axis).
         legend (str, optional): Text for the plot legend. Defaults to None.
-        **kwds: Keyword arguments for ``bokeh.plotting.figure().quad()``.
+        **kwds:
+            - *tooltip*: Tooltip formatting tuple. Defaults to [("(x, y)", "($x, $y)")]
+
+            Additional keyword arguments are passed to ``bokeh.plotting.figure().quad()``.
 
     Returns:
         pbk.figure: An instance of 'bokeh.plotting.figure' as a plot handle.
@@ -54,8 +58,8 @@ def grid_histogram(
     ncol: int,
     rvs: Sequence,
     rvbins: Sequence,
-    rvranges: Sequence[Tuple[float, float]],
-    backend: str = "bokeh",
+    rvranges: Sequence[tuple[float, float]],
+    backend: str = "matplotlib",
     legend: bool = True,
     histkwds: dict = None,
     legkwds: dict = None,
@@ -68,35 +72,42 @@ def grid_histogram(
         ncol (int): Number of columns in the plot grid.
         rvs (Sequence): List of names for the random variables (rvs).
         rvbins (Sequence): Bin values for all random variables.
-        rvranges (Sequence[Tuple[float, float]]): Value ranges of all random variables.
-        backend (str, optional): Backend for making the plot ('matplotlib' or 'bokeh').
-            Defaults to "bokeh".
+        rvranges (Sequence[tuple[float, float]]): Value ranges of all random variables.
+        backend (str, optional): Backend for making the plot ("matplotlib" or "bokeh").
+            Defaults to "matplotlib".
         legend (bool, optional): Option to include a legend in each histogram plot.
             Defaults to True.
         histkwds (dict, optional): Keyword arguments for histogram plots.
             Defaults to None.
         legkwds (dict, optional): Keyword arguments for legends. Defaults to None.
-        **kwds: Additional keyword arguments.
+        **kwds:
+            - *figsize*: Figure size. Defaults to (6, 4)
     """
     if histkwds is None:
         histkwds = {}
     if legkwds is None:
         legkwds = {}
 
-    figsz = kwds.pop("figsize", (14, 8))
+    figsz = kwds.pop("figsize", (3, 2))  # figure size of each panel
+
+    if len(kwds) > 0:
+        raise TypeError(f"grid_histogram() got unexpected keyword arguments {kwds.keys()}.")
 
     if backend == "matplotlib":
         nrv = len(rvs)
         nrow = int(np.ceil(nrv / ncol))
-        histtype = kwds.pop("histtype", "step")
+        histtype = kwds.pop("histtype", "bar")
 
-        fig, ax = plt.subplots(nrow, ncol, figsize=figsz)
+        figsize = [figsz[0] * ncol, figsz[1] * nrow]
+        fig, ax = plt.subplots(nrow, ncol, figsize=figsize)
         otherax = ax.copy()
         for i, zipped in enumerate(zip(rvs, rvbins, rvranges)):
             # Make each histogram plot
             rvname, rvbin, rvrg = zipped
             try:
                 axind = np.unravel_index(i, (nrow, ncol))
+                plt.setp(ax[axind].get_xticklabels(), fontsize=8)
+                plt.setp(ax[axind].get_yticklabels(), fontsize=8)
                 ax[axind].hist(
                     dct[rvname],
                     bins=rvbin,
@@ -106,11 +117,13 @@ def grid_histogram(
                     **histkwds,
                 )
                 if legend:
-                    ax[axind].legend(fontsize=15, **legkwds)
+                    ax[axind].legend(fontsize=10, **legkwds)
 
                 otherax[axind] = None
 
             except IndexError:
+                plt.setp(ax[i].get_xticklabels(), fontsize=8)
+                plt.setp(ax[i].get_yticklabels(), fontsize=8)
                 ax[i].hist(
                     dct[rvname],
                     bins=rvbin,
@@ -120,7 +133,7 @@ def grid_histogram(
                     **histkwds,
                 )
                 if legend:
-                    ax[i].legend(fontsize=15, **legkwds)
+                    ax[i].legend(fontsize=10, **legkwds)
 
                 otherax[i] = None
 
@@ -128,6 +141,8 @@ def grid_histogram(
             if oax is not None:
                 fig.delaxes(oax)
 
+        plt.tight_layout()
+
     elif backend == "bokeh":
         output_notebook(hide_banner=True)
 
@@ -155,7 +170,7 @@ def grid_histogram(
             gridplot(
                 plots,  # type: ignore
                 ncols=ncol,
-                width=figsz[0] * 30,
-                height=figsz[1] * 28,
+                width=figsz[0] * 100,
+                height=figsz[1] * 100,
             ),
         )
diff --git a/sed/io/__init__.py b/src/sed/io/__init__.py
similarity index 100%
rename from sed/io/__init__.py
rename to src/sed/io/__init__.py
diff --git a/sed/io/hdf5.py b/src/sed/io/hdf5.py
similarity index 97%
rename from sed/io/hdf5.py
rename to src/sed/io/hdf5.py
index 5d4d45d8..1b6ab35a 100644
--- a/sed/io/hdf5.py
+++ b/src/sed/io/hdf5.py
@@ -1,7 +1,7 @@
 """This module contains hdf5 file input/output functions for the sed.io module
 
 """
-from typing import Union
+from __future__ import annotations
 
 import h5py
 import numpy as np
@@ -53,12 +53,12 @@ def recursive_write_metadata(h5group: h5py.Group, node: dict):
 
 
 def recursive_parse_metadata(
-    node: Union[h5py.Group, h5py.Dataset],
+    node: h5py.Group | h5py.Dataset,
 ) -> dict:
     """Recurses through an hdf5 file, and parse it into a dictionary.
 
     Args:
-        node (Union[h5py.Group, h5py.Dataset]): hdf5 group or dataset to parse into
+        node (h5py.Group | h5py.Dataset): hdf5 group or dataset to parse into
             dictionary.
 
     Returns:
diff --git a/sed/io/nexus.py b/src/sed/io/nexus.py
similarity index 85%
rename from sed/io/nexus.py
rename to src/sed/io/nexus.py
index 2ccaccc4..2041647c 100644
--- a/sed/io/nexus.py
+++ b/src/sed/io/nexus.py
@@ -3,8 +3,9 @@
 For details, see https://github.com/nomad-coe/nomad-parser-nexus
 
 """
-from typing import Sequence
-from typing import Union
+from __future__ import annotations
+
+from collections.abc import Sequence
 
 import xarray as xr
 from pynxtools.dataconverter.convert import convert
@@ -15,7 +16,7 @@ def to_nexus(
     faddr: str,
     reader: str,
     definition: str,
-    input_files: Union[str, Sequence[str]],
+    input_files: str | Sequence[str],
     **kwds,
 ):
     """Saves the x-array provided to a NeXus file at faddr, using the provided reader,
@@ -27,8 +28,7 @@ def to_nexus(
         faddr (str): The file path to save to.
         reader (str): The name of the NeXus reader to use.
         definition (str): The NeXus definition to use.
-        input_files (Union[str, Sequence[str]]): The file path or paths to the additional files to
-            use.
+        input_files (str | Sequence[str]): The file path or paths to the additional files to use.
         **kwds: Keyword arguments for ``pynxtools.dataconverter.convert.convert()``.
     """
 
diff --git a/sed/io/tiff.py b/src/sed/io/tiff.py
similarity index 94%
rename from sed/io/tiff.py
rename to src/sed/io/tiff.py
index 26f0f23f..4de1a42c 100644
--- a/sed/io/tiff.py
+++ b/src/sed/io/tiff.py
@@ -1,9 +1,10 @@
 """This module contains tiff file input/output functions for the sed.io module
 
 """
+from __future__ import annotations
+
+from collections.abc import Sequence
 from pathlib import Path
-from typing import Sequence
-from typing import Union
 
 import numpy as np
 import tifffile
@@ -37,20 +38,20 @@
 
 
 def to_tiff(
-    data: Union[xr.DataArray, np.ndarray],
-    faddr: Union[Path, str],
+    data: xr.DataArray | np.ndarray,
+    faddr: Path | str,
     alias_dict: dict = None,
 ):
     """Save an array as a .tiff stack compatible with ImageJ
 
     Args:
-        data (Union[xr.DataArray, np.ndarray]): data to be saved. If a np.ndarray,
+        data (xr.DataArray | np.ndarray): data to be saved. If a np.ndarray,
             the order is retained. If it is an xarray.DataArray, the order is inferred
             from axis_dict instead. ImageJ likes tiff files with axis order as
             TZCYXS. Therefore, best axis order in input should be: Time, Energy,
             posY, posX. The channels 'C' and 'S' are automatically added and can
             be ignored.
-        faddr (Union[Path, str]): full path and name of file to save.
+        faddr Path | str): full path and name of file to save.
         alias_dict (dict, optional): name pairs for correct axis ordering. Keys should
             be any of T,Z,C,Y,X,S. The Corresponding value should be a dimension of the
             xarray or the dimension number if a numpy array. This is used to sort the
@@ -63,7 +64,7 @@ def to_tiff(
         NotImplementedError: if data is not 2,3 or 4 dimensional
         TypeError: if data is not a np.ndarray or an xarray.DataArray
     """
-    out: Union[np.ndarray, xr.DataArray] = None
+    out: np.ndarray | xr.DataArray = None
     if isinstance(data, np.ndarray):
         # TODO: add sorting by dictionary keys
         dim_expansions = {2: [0, 1, 2, 5], 3: [0, 2, 5], 4: [2, 5]}
@@ -172,7 +173,7 @@ def _fill_missing_dims(dims: Sequence, alias_dict: dict = None) -> list:
 
 
 def load_tiff(
-    faddr: Union[str, Path],
+    faddr: str | Path,
     coords: dict = None,
     dims: Sequence = None,
     attrs: dict = None,
@@ -184,7 +185,7 @@ def load_tiff(
     only as np.ndarray.
 
     Args:
-        faddr (Union[str, Path]): Path to file to load.
+        faddr (str | Path): Path to file to load.
         coords (dict, optional): The axes describing the data, following the tiff
             stack order. Defaults to None.
         dims (Sequence, optional): the order of the coordinates provided, considering
diff --git a/sed/loader/__init__.py b/src/sed/loader/__init__.py
similarity index 100%
rename from sed/loader/__init__.py
rename to src/sed/loader/__init__.py
diff --git a/sed/loader/base/README.md b/src/sed/loader/base/README.md
similarity index 100%
rename from sed/loader/base/README.md
rename to src/sed/loader/base/README.md
diff --git a/sed/loader/base/__init__.py b/src/sed/loader/base/__init__.py
similarity index 100%
rename from sed/loader/base/__init__.py
rename to src/sed/loader/base/__init__.py
diff --git a/sed/loader/base/loader.py b/src/sed/loader/base/loader.py
similarity index 72%
rename from sed/loader/base/loader.py
rename to src/sed/loader/base/loader.py
index 4e64e528..d2848d23 100644
--- a/sed/loader/base/loader.py
+++ b/src/sed/loader/base/loader.py
@@ -1,14 +1,13 @@
-"""The abstract class off of which to implement loaders."""
+"""The abstract class off of which to implement loaders.
+"""
+from __future__ import annotations
+
 import os
 from abc import ABC
 from abc import abstractmethod
+from collections.abc import Sequence
 from copy import deepcopy
 from typing import Any
-from typing import Dict
-from typing import List
-from typing import Sequence
-from typing import Tuple
-from typing import Union
 
 import dask.dataframe as ddf
 import numpy as np
@@ -25,46 +24,65 @@ class BaseLoader(ABC):
 
     Args:
         config (dict, optional): Config dictionary. Defaults to None.
-        meta_handler (MetaHandler, optional): MetaHandler object. Defaults to None.
+        verbose (bool, optional): Option to print out diagnostic information.
+            Defaults to True.
     """
 
-    # pylint: disable=too-few-public-methods
-
     __name__ = "BaseLoader"
 
-    supported_file_types: List[str] = []
+    supported_file_types: list[str] = []
 
     def __init__(
         self,
         config: dict = None,
+        verbose: bool = True,
     ):
         self._config = config if config is not None else {}
 
-        self.files: List[str] = []
-        self.runs: List[str] = []
-        self.metadata: Dict[Any, Any] = {}
+        self.files: list[str] = []
+        self.runs: list[str] = []
+        self.metadata: dict[Any, Any] = {}
+        self._verbose = verbose
+
+    @property
+    def verbose(self) -> bool:
+        """Accessor to the verbosity flag.
+
+        Returns:
+            bool: Verbosity flag.
+        """
+        return self._verbose
+
+    @verbose.setter
+    def verbose(self, verbose: bool):
+        """Setter for the verbosity.
+
+        Args:
+            verbose (bool): Option to turn on verbose output. Sets loglevel to INFO.
+        """
+        self._verbose = verbose
 
     @abstractmethod
     def read_dataframe(
         self,
-        files: Union[str, Sequence[str]] = None,
-        folders: Union[str, Sequence[str]] = None,
-        runs: Union[str, Sequence[str]] = None,
+        files: str | Sequence[str] = None,
+        folders: str | Sequence[str] = None,
+        runs: str | Sequence[str] = None,
         ftype: str = None,
         metadata: dict = None,
         collect_metadata: bool = False,
         **kwds,
-    ) -> Tuple[ddf.DataFrame, ddf.DataFrame, dict]:
+    ) -> tuple[ddf.DataFrame, ddf.DataFrame, dict]:
         """Reads data from given files, folder, or runs and returns a dask dataframe
         and corresponding metadata.
 
         Args:
-            files (Union[str, Sequence[str]], optional): File path(s) to process.
+            files (str | Sequence[str], optional): File path(s) to process.
                 Defaults to None.
-            folders (Union[str, Sequence[str]], optional): Path to folder(s) where files
+            folders (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
+            runs (str | Sequence[str], optional): Run identifier(s). Corresponding
                 files will be located in the location provided by ``folders``. Takes
                 precedence over ``files`` and ``folders``. Defaults to None.
             ftype (str, optional): File type to read ('parquet', 'json', 'csv', etc).
@@ -77,7 +95,7 @@ def read_dataframe(
             **kwds: keyword arguments. See description in respective loader.
 
         Returns:
-            Tuple[ddf.DataFrame, dict]: Dask dataframe, timed dataframe and metadata
+            tuple[ddf.DataFrame, ddf.DataFrame, dict]: Dask dataframe, timed dataframe and metadata
             read from specified files.
         """
 
@@ -109,7 +127,7 @@ def read_dataframe(
 
         elif files is None:
             raise ValueError(
-                "Either folder, file paths, or runs should be provided!",
+                "Either folders, files, or runs have to be provided!",
             )
 
         if files is not None:
@@ -129,21 +147,21 @@ def read_dataframe(
     def get_files_from_run_id(
         self,
         run_id: str,
-        folders: Union[str, Sequence[str]] = None,
+        folders: str | Sequence[str] = None,
         extension: str = None,
         **kwds,
-    ) -> List[str]:
+    ) -> list[str]:
         """Locate the files for a given run identifier.
 
         Args:
             run_id (str): The run identifier to locate.
-            folders (Union[str, Sequence[str]], optional): The directory(ies) where the raw
+            folders (str | Sequence[str], optional): The directory(ies) where the raw
                 data is located. Defaults to None.
             extension (str, optional): The file extension. Defaults to None.
             kwds: Keyword arguments
 
         Return:
-            List[str]: List of files for the given run.
+            list[str]: List of files for the given run.
         """
         raise NotImplementedError
 
@@ -152,7 +170,7 @@ def get_count_rate(
         self,
         fids: Sequence[int] = None,
         **kwds,
-    ) -> Tuple[np.ndarray, np.ndarray]:
+    ) -> tuple[np.ndarray, np.ndarray]:
         """Create count rate data for the files specified in ``fids``.
 
         Args:
@@ -161,7 +179,7 @@ def get_count_rate(
             kwds: Keyword arguments
 
         Return:
-            Tuple[np.ndarray, np.ndarray]: Arrays containing countrate and seconds
+            tuple[np.ndarray, np.ndarray]: Arrays containing countrate and seconds
             into the scan.
         """
         return None, None
diff --git a/sed/loader/flash/__init__.py b/src/sed/loader/flash/__init__.py
similarity index 100%
rename from sed/loader/flash/__init__.py
rename to src/sed/loader/flash/__init__.py
diff --git a/src/sed/loader/flash/buffer_handler.py b/src/sed/loader/flash/buffer_handler.py
new file mode 100644
index 00000000..d56de29f
--- /dev/null
+++ b/src/sed/loader/flash/buffer_handler.py
@@ -0,0 +1,336 @@
+from __future__ import annotations
+
+import os
+from pathlib import Path
+import time
+
+import dask.dataframe as dd
+import pyarrow.parquet as pq
+from joblib import delayed
+from joblib import Parallel
+
+from sed.core.dfops import forward_fill_lazy
+from sed.core.logging import setup_logging
+from sed.loader.flash.dataframe import DataFrameCreator
+from sed.loader.flash.utils import get_channels
+from sed.loader.flash.utils import get_dtypes
+from sed.loader.flash.utils import InvalidFileError
+from sed.loader.utils import get_parquet_metadata
+from sed.loader.utils import split_dld_time_from_sector_id
+
+
+DF_TYP = ["electron", "timed"]
+
+logger = setup_logging("flash_buffer_handler")
+
+
+class BufferFilePaths:
+    """
+    A class for handling the paths to the raw and buffer files of electron and timed dataframes.
+    A list of file sets (dict) are created for each H5 file containing the paths to the raw file
+    and the electron and timed buffer files.
+
+    Structure of the file sets:
+    {
+        "raw": Path to the H5 file,
+        "electron": Path to the electron buffer file,
+        "timed": Path to the timed buffer file,
+    }
+    """
+
+    def __init__(
+        self,
+        config: dict,
+        h5_paths: list[Path],
+        folder: Path,
+        suffix: str,
+        remove_invalid_files: bool,
+    ) -> None:
+        """Initializes the BufferFilePaths.
+
+        Args:
+            h5_paths (list[Path]): List of paths to the H5 files.
+            folder (Path): Path to the folder for processed files.
+            suffix (str): Suffix for buffer file names.
+        """
+        suffix = f"_{suffix}" if suffix else ""
+        folder = folder / "buffer"
+        folder.mkdir(parents=True, exist_ok=True)
+
+        if remove_invalid_files:
+            h5_paths = self.remove_invalid_files(config, h5_paths)
+
+        self._file_paths = self._create_file_paths(h5_paths, folder, suffix)
+
+    def _create_file_paths(
+        self,
+        h5_paths: list[Path],
+        folder: Path,
+        suffix: str,
+    ) -> list[dict[str, Path]]:
+        return [
+            {
+                "raw": h5_path,
+                **{typ: folder / f"{typ}_{h5_path.stem}{suffix}" for typ in DF_TYP},
+            }
+            for h5_path in h5_paths
+        ]
+
+    def __getitem__(self, key) -> list[Path]:
+        if isinstance(key, str):
+            return [file_set[key] for file_set in self._file_paths]
+        return self._file_paths[key]
+
+    def __iter__(self):
+        return iter(self._file_paths)
+
+    def __len__(self):
+        return len(self._file_paths)
+
+    def file_sets_to_process(self, force_recreate: bool = False) -> list[dict[str, Path]]:
+        """Returns a list of file sets that need to be processed."""
+        if force_recreate:
+            return self._file_paths
+        return [file_set for file_set in self if any(not file_set[key].exists() for key in DF_TYP)]
+
+    def remove_invalid_files(self, config, h5_paths: list[Path]) -> list[Path]:
+        valid_h5_paths = []
+        for h5_path in h5_paths:
+            try:
+                dfc = DataFrameCreator(config_dataframe=config, h5_path=h5_path)
+                dfc.validate_channel_keys()
+                valid_h5_paths.append(h5_path)
+            except InvalidFileError as e:
+                logger.info(f"Skipping invalid file: {h5_path.stem}\n{e}")
+
+        return valid_h5_paths
+
+
+class BufferHandler:
+    """
+    A class for handling the creation and manipulation of buffer files using DataFrameCreator.
+    """
+
+    def __init__(
+        self,
+        config: dict,
+    ) -> None:
+        """
+        Initializes the BufferHandler.
+
+        Args:
+            config (dict): The configuration dictionary.
+        """
+        self._config: dict = config["dataframe"]
+        self.n_cores: int = config["core"].get("num_cores", os.cpu_count() - 1)
+        self.fp: BufferFilePaths = None
+        self.df: dict[str, dd.DataFrame] = {typ: None for typ in DF_TYP}
+        self.fill_channels: list[str] = get_channels(
+            self._config,
+            ["per_pulse", "per_train"],
+            extend_aux=True,
+        )
+        self.metadata: dict = {}
+        self.filter_timed_by_electron: bool = None
+
+    def _schema_check(self, files: list[Path], expected_schema_set: set) -> None:
+        """
+        Checks the schema of the Parquet files.
+        """
+        logger.debug(f"Checking schema for {len(files)} files")
+        existing = [file for file in files if file.exists()]
+        parquet_schemas = [pq.read_schema(file) for file in existing]
+
+        for filename, schema in zip(existing, parquet_schemas):
+            schema_set = set(schema.names)
+            if schema_set != expected_schema_set:
+                logger.error(f"Schema mismatch in file: {filename}")
+                missing_in_parquet = expected_schema_set - schema_set
+                missing_in_config = schema_set - expected_schema_set
+
+                errors = []
+                if missing_in_parquet:
+                    errors.append(f"Missing in parquet: {missing_in_parquet}")
+                if missing_in_config:
+                    errors.append(f"Missing in config: {missing_in_config}")
+
+                raise ValueError(
+                    f"The available channels do not match the schema of file {filename}. "
+                    f"{' '.join(errors)}. "
+                    "Please check the configuration file or set force_recreate to True.",
+                )
+        logger.debug("Schema check passed successfully")
+
+    def _create_timed_dataframe(self, df: dd.DataFrame) -> dd.DataFrame:
+        """Creates the timed dataframe, optionally filtering by electron events.
+
+        Args:
+            df (dd.DataFrame): The input dataframe containing all data
+
+        Returns:
+            dd.DataFrame: The timed dataframe
+        """
+        # Get channels that should be in timed dataframe
+        timed_channels = self.fill_channels
+
+        if self.filter_timed_by_electron:
+            # Get electron channels to use for filtering
+            electron_channels = get_channels(self._config, "per_electron")
+            # Filter rows where electron data exists
+            df_timed = df.dropna(subset=electron_channels)[timed_channels]
+        else:
+            # Take all timed data rows without filtering
+            df_timed = df[timed_channels]
+
+        # Take only first electron per event
+        return df_timed.loc[:, :, 0]
+
+    def _save_buffer_file(self, paths: dict[str, Path]) -> None:
+        """Creates the electron and timed buffer files from the raw H5 file."""
+        logger.debug(f"Processing file: {paths['raw'].stem}")
+        start_time = time.time()
+        # Create DataFrameCreator and get get dataframe
+        df = DataFrameCreator(config_dataframe=self._config, h5_path=paths["raw"]).df
+
+        # Forward fill non-electron channels
+        logger.debug(f"Forward filling {len(self.fill_channels)} channels")
+        df[self.fill_channels] = df[self.fill_channels].ffill()
+
+        # Save electron resolved dataframe
+        electron_channels = get_channels(self._config, "per_electron")
+        dtypes = get_dtypes(self._config, df.columns.values)
+        electron_df = df.dropna(subset=electron_channels).astype(dtypes).reset_index()
+        logger.debug(f"Saving electron buffer with shape: {electron_df.shape}")
+        electron_df.to_parquet(paths["electron"])
+
+        # Create and save timed dataframe
+        df_timed = self._create_timed_dataframe(df)
+        dtypes = get_dtypes(self._config, df_timed.columns.values)
+        timed_df = df_timed.astype(dtypes).reset_index()
+        logger.debug(f"Saving timed buffer with shape: {timed_df.shape}")
+        timed_df.to_parquet(paths["timed"])
+
+        logger.debug(f"Processed {paths['raw'].stem} in {time.time() - start_time:.2f}s")
+
+    def _save_buffer_files(self, force_recreate: bool, debug: bool) -> None:
+        """
+        Creates the buffer files that are missing.
+
+        Args:
+            force_recreate (bool): Flag to force recreation of buffer files.
+            debug (bool): Flag to enable debug mode, which serializes the creation.
+        """
+        file_sets = self.fp.file_sets_to_process(force_recreate)
+        logger.info(f"Reading files: {len(file_sets)} new files of {len(self.fp)} total.")
+        n_cores = min(len(file_sets), self.n_cores)
+        if n_cores > 0:
+            if debug:
+                for file_set in file_sets:
+                    self._save_buffer_file(file_set)
+            else:
+                Parallel(n_jobs=n_cores, verbose=10)(
+                    delayed(self._save_buffer_file)(file_set) for file_set in file_sets
+                )
+
+    def _get_dataframes(self) -> None:
+        """
+        Reads the buffer files from a folder.
+
+        First the buffer files are read as a dask dataframe is accessed.
+        The dataframe is forward filled lazily with non-electron channels.
+        For the electron dataframe, all values not in the electron channels
+        are dropped, and splits the sector ID from the DLD time.
+        For the timed dataframe, only the train and pulse channels are taken and
+        it pulse resolved (no longer electron resolved). If time_index is True,
+        the timeIndex is calculated and set as the index (slow operation).
+        """
+        if not self.fp:
+            raise FileNotFoundError("Buffer files do not exist.")
+        # Loop over the electron and timed dataframes
+        file_stats = {}
+        filling = {}
+        for typ in DF_TYP:
+            # Read the parquet files into a dask dataframe
+            df = dd.read_parquet(self.fp[typ], calculate_divisions=True)
+            # Get the metadata from the parquet files
+            file_stats[typ] = get_parquet_metadata(self.fp[typ])
+
+            # Forward fill the non-electron channels across files
+            overlap = min(file["num_rows"] for file in file_stats[typ].values())
+            iterations = self._config.get("forward_fill_iterations", 2)
+            df = forward_fill_lazy(
+                df=df,
+                columns=self.fill_channels,
+                before=overlap,
+                iterations=iterations,
+            )
+            # TODO: This dict should be returned by forward_fill_lazy
+            filling[typ] = {
+                "columns": self.fill_channels,
+                "overlap": overlap,
+                "iterations": iterations,
+            }
+
+            self.df[typ] = df
+        self.metadata.update({"file_statistics": file_stats, "filling": filling})
+        # Correct the 3-bit shift which encodes the detector ID in the 8s time
+        if (
+            self._config.get("split_sector_id_from_dld_time", False)
+            and self._config.get("tof_column", "dldTimeSteps") in self.df["electron"].columns
+        ):
+            self.df["electron"], meta = split_dld_time_from_sector_id(
+                self.df["electron"],
+                config=self._config,
+            )
+            self.metadata.update(meta)
+
+    def process_and_load_dataframe(
+        self,
+        h5_paths: list[Path],
+        folder: Path,
+        force_recreate: bool = False,
+        suffix: str = "",
+        debug: bool = False,
+        remove_invalid_files: bool = False,
+        filter_timed_by_electron: bool = True,
+    ) -> tuple[dd.DataFrame, dd.DataFrame]:
+        """
+        Runs the buffer file creation process.
+        Does a schema check on the buffer files and creates them if they are missing.
+        Performs forward filling and splits the sector ID from the DLD time lazily.
+
+        Args:
+            h5_paths (List[Path]): List of paths to H5 files.
+            folder (Path): Path to the folder for processed files.
+            force_recreate (bool): Flag to force recreation of buffer files.
+            suffix (str): Suffix for buffer file names.
+            debug (bool): Flag to enable debug mode.):
+            remove_invalid_files (bool): Flag to remove invalid files.
+            filter_timed_by_electron (bool): Flag to filter timed data by valid electron events.
+
+        Returns:
+            Tuple[dd.DataFrame, dd.DataFrame]: The electron and timed dataframes.
+        """
+        self.fp = BufferFilePaths(self._config, h5_paths, folder, suffix, remove_invalid_files)
+        self.filter_timed_by_electron = filter_timed_by_electron
+
+        if not force_recreate:
+            schema_set = set(
+                get_channels(self._config, formats="all", index=True, extend_aux=True),
+            )
+            self._schema_check(self.fp["electron"], schema_set)
+            schema_set = set(
+                get_channels(
+                    self._config,
+                    formats=["per_pulse", "per_train"],
+                    index=True,
+                    extend_aux=True,
+                ),
+            ) - {"electronId"}
+            self._schema_check(self.fp["timed"], schema_set)
+
+        self._save_buffer_files(force_recreate, debug)
+
+        self._get_dataframes()
+
+        return self.df["electron"], self.df["timed"]
diff --git a/src/sed/loader/flash/dataframe.py b/src/sed/loader/flash/dataframe.py
new file mode 100644
index 00000000..f50abe10
--- /dev/null
+++ b/src/sed/loader/flash/dataframe.py
@@ -0,0 +1,310 @@
+"""
+This module creates pandas DataFrames from HDF5 files for different levels of data granularity
+[per electron, per pulse, and per train]. It efficiently handles concatenation of data from
+various channels within the HDF5 file, making use of the structured nature data to optimize
+join operations. This approach significantly enhances performance compared to earlier.
+"""
+from __future__ import annotations
+
+from pathlib import Path
+
+import h5py
+import numpy as np
+import pandas as pd
+
+from sed.loader.flash.utils import get_channels
+from sed.loader.flash.utils import InvalidFileError
+from sed.core.logging import setup_logging
+
+logger = setup_logging("flash_dataframe_creator")
+
+
+class DataFrameCreator:
+    """
+    A class for creating pandas DataFrames from an HDF5 file.
+
+    Attributes:
+        h5_file (h5py.File): The HDF5 file object.
+        multi_index (pd.MultiIndex): The multi-index structure for the DataFrame.
+        _config (dict): The configuration dictionary for the DataFrame.
+    """
+
+    def __init__(self, config_dataframe: dict, h5_path: Path) -> None:
+        """
+        Initializes the DataFrameCreator class.
+
+        Args:
+            config_dataframe (dict): The configuration dictionary with only the dataframe key.
+            h5_path (Path): Path to the h5 file.
+        """
+        logger.debug(f"Initializing DataFrameCreator for file: {h5_path}")
+        self.h5_file = h5py.File(h5_path, "r")
+        self.multi_index = get_channels(index=True)
+        self._config = config_dataframe
+
+    def get_index_dataset_key(self, channel: str) -> tuple[str, str]:
+        """
+        Checks if 'index_key' and 'dataset_key' exists and returns that.
+
+        Args:
+            channel (str): The name of the channel.
+
+        Returns:
+            tuple[str, str]: Outputs a tuple of 'index_key' and 'dataset_key'.
+
+        Raises:
+            ValueError: If 'index_key' and 'dataset_key' are not provided.
+        """
+        channel_config = self._config["channels"][channel]
+        group_err = ""
+        if "index_key" in channel_config and "dataset_key" in channel_config:
+            return channel_config["index_key"], channel_config["dataset_key"]
+        elif "group_name" in channel_config:
+            group_err = "'group_name' is no longer supported."
+        error = f"{group_err} For channel: {channel}, provide both 'index_key' and 'dataset_key'."
+        raise ValueError(error)
+
+    def get_dataset_array(
+        self,
+        channel: str,
+        slice_: bool = True,
+    ) -> tuple[pd.Index, np.ndarray | h5py.Dataset]:
+        """
+        Returns a numpy array for a given channel name.
+
+        Args:
+            channel (str): The name of the channel.
+            slice_ (bool): Applies slicing on the dataset. Default is True.
+
+        Returns:
+            tuple[pd.Index, np.ndarray | h5py.Dataset]: A tuple containing the train ID
+            pd.Index and the channel's data.
+        """
+        logger.debug(f"Getting dataset array for channel: {channel}")
+        # Get the data from the necessary h5 file and channel
+        index_key, dataset_key = self.get_index_dataset_key(channel)
+
+        key = pd.Index(self.h5_file[index_key], name="trainId")  # macrobunch
+        dataset = self.h5_file[dataset_key]
+
+        if slice_:
+            slice_index = self._config["channels"][channel].get("slice", None)
+            if slice_index is not None:
+                logger.debug(f"Slicing dataset with index: {slice_index}")
+                dataset = np.take(dataset, slice_index, axis=1)
+        # If np_array is size zero, fill with NaNs, fill it with NaN values
+        # of the same shape as index
+        if dataset.shape[0] == 0:
+            dataset = np.full_like(key, np.nan, dtype=np.double)
+
+        return key, dataset
+
+    def pulse_index(self, offset: int) -> tuple[pd.MultiIndex, slice | np.ndarray]:
+        """
+        Creates a multi-level index that combines train IDs and pulse IDs, and handles
+        sorting and electron counting within each pulse.
+
+        Args:
+            offset (int): The offset value.
+
+        Returns:
+            tuple[pd.MultiIndex, np.ndarray]: A tuple containing the computed pd.MultiIndex and
+            the indexer.
+        """
+        # Get the pulse_dataset and the train_index
+        train_index, pulse_dataset = self.get_dataset_array("pulseId")
+        # pulse_dataset comes as a 2D array, resolved per train. Here it is flattened
+        # the daq has an offset so no pulses are missed. This offset is subtracted here
+        pulse_ravel = pulse_dataset.ravel() - offset
+        # Here train_index is repeated to match the size of pulses
+        train_index_repeated = np.repeat(train_index, pulse_dataset.shape[1])
+        # A pulse resolved multi-index is finally created.
+        # Since there can be NaN pulses, those are dropped
+        pulse_index = pd.MultiIndex.from_arrays((train_index_repeated, pulse_ravel)).dropna()
+
+        # Sometimes the pulse_index are not monotonic, so we might need to sort them
+        # The indexer is also returned to sort the data in df_electron
+        indexer = slice(None)
+        if not pulse_index.is_monotonic_increasing:
+            pulse_index, indexer = pulse_index.sort_values(return_indexer=True)
+
+        # In the data, to signify different electrons, pulse_index is repeated by
+        # the number of electrons in each pulse. Here the values are counted
+        electron_counts = pulse_index.value_counts(sort=False).values
+        # Now we resolve each pulse to its electrons
+        electron_index = np.concatenate(
+            [np.arange(count, dtype="uint16") for count in electron_counts],
+        )
+
+        # Final multi-index constructed here
+        index = pd.MultiIndex.from_arrays(
+            (
+                pulse_index.get_level_values(0),
+                pulse_index.get_level_values(1).astype(int),
+                electron_index,
+            ),
+            names=self.multi_index,
+        )
+        return index, indexer
+
+    @property
+    def df_electron(self) -> pd.DataFrame:
+        """
+        Returns a pandas DataFrame for channel names of type [per electron].
+
+        Returns:
+            pd.DataFrame: The pandas DataFrame for the 'per_electron' channel's data.
+        """
+        # Get the relevant channels and their slice index
+        channels = get_channels(self._config, "per_electron")
+        if channels == []:
+            return pd.DataFrame()
+        slice_index = [self._config["channels"][channel].get("slice", None) for channel in channels]
+
+        offset = self._config.get("ubid_offset", 5)  # 5 is the default value
+        # Here we get the multi-index and the indexer to sort the data
+        index, indexer = self.pulse_index(offset)
+
+        # First checking if dataset keys are the same for all channels
+        # because DLD at FLASH stores all channels in the same h5 dataset
+        dataset_keys = [self.get_index_dataset_key(channel)[1] for channel in channels]
+        # Gives a true if all keys are the same
+        all_keys_same = all(key == dataset_keys[0] for key in dataset_keys)
+
+        # If all dataset keys are the same, we only need to load the dataset once and slice
+        # the appropriate columns. This is much faster than loading the same dataset multiple times
+        if all_keys_same:
+            _, dataset = self.get_dataset_array(channels[0], slice_=False)
+            data_dict = {
+                channel: dataset[:, idx, :].ravel() for channel, idx in zip(channels, slice_index)
+            }
+            dataframe = pd.DataFrame(data_dict)
+        # In case channels do differ, we create a pd.Series for each channel and concatenate them
+        else:
+            series = {
+                channel: pd.Series(self.get_dataset_array(channel)[1].ravel())
+                for channel in channels
+            }
+            dataframe = pd.concat(series, axis=1)
+
+        # NaN values dropped, data sorted with [indexer] if necessary, and the MultiIndex is set
+        return dataframe.dropna().iloc[indexer].set_index(index)
+
+    @property
+    def df_pulse(self) -> pd.DataFrame:
+        """
+        Returns a pandas DataFrame for given channel names of type [per pulse].
+
+        Returns:
+            pd.DataFrame: The pandas DataFrame for the 'per_pulse' channel's data.
+        """
+        series = []
+        # Get the relevant channel names
+        channels = get_channels(self._config, "per_pulse")
+        if channels == []:
+            return pd.DataFrame()
+        # For each channel, a pd.Series is created and appended to the list
+        for channel in channels:
+            # train_index and (sliced) data is returned
+            key, dataset = self.get_dataset_array(channel)
+            # Electron resolved MultiIndex is created. Since this is pulse data,
+            # the electron index is always 0
+            index = pd.MultiIndex.from_product(
+                (key, np.arange(0, dataset.shape[1]), [0]),
+                names=self.multi_index,
+            )
+            # The dataset is opened and converted to numpy array by [()]
+            # and flattened to resolve per pulse
+            channel_series = pd.Series(dataset[()].ravel(), index=index, name=channel)
+            # sometimes pulse columns have more pulses than valid ones such as with bam channel
+            # so we remove all 0 values from the series
+            series.append(channel_series[channel_series != 0])  # TODO: put this in metadata
+
+        # All the channels are concatenated to a single DataFrame
+        return pd.concat(
+            series,
+            axis=1,
+        )
+
+    @property
+    def df_train(self) -> pd.DataFrame:
+        """
+        Returns a pandas DataFrame for given channel names of type [per train].
+
+        Returns:
+            pd.DataFrame: The pandas DataFrame for the 'per_train' channel's data.
+        """
+        series = []
+        # Get the relevant channel names
+        channels = get_channels(self._config, "per_train")
+        # For each channel, a pd.Series is created and appended to the list
+        for channel in channels:
+            # train_index and (sliced) data is returned
+            key, dataset = self.get_dataset_array(channel)
+            # Electron and pulse resolved MultiIndex is created. Since this is train data,
+            # the electron and pulse index is always 0
+            index = pd.MultiIndex.from_product(
+                (key, [0], [0]),
+                names=self.multi_index,
+            )
+            # Auxiliary dataset (which is stored in the same dataset as other DLD channels)
+            # contains multiple channels inside. Even though they are resolved per train,
+            # they come in pulse format, so the extra values are sliced and individual channels are
+            # created and appended to the list
+            aux_alias = self._config.get("aux_alias", "dldAux")
+            if channel == aux_alias:
+                try:
+                    sub_channels = self._config["channels"][aux_alias]["sub_channels"]
+                except KeyError:
+                    raise KeyError(
+                        f"Provide 'subChannels' for auxiliary channel '{aux_alias}'.",
+                    )
+                for name, values in sub_channels.items():
+                    series.append(
+                        pd.Series(
+                            dataset[: key.size, values["slice"]],
+                            index,
+                            name=name,
+                        ),
+                    )
+            else:
+                series.append(pd.Series(dataset, index, name=channel))
+        # All the channels are concatenated to a single DataFrame
+        return pd.concat(series, axis=1)
+
+    def validate_channel_keys(self) -> None:
+        """
+        Validates if the index and dataset keys for all channels in the config exist in the h5 file.
+
+        Raises:
+            InvalidFileError: If the index or dataset keys are missing in the h5 file.
+        """
+        invalid_channels = []
+        for channel in self._config["channels"]:
+            index_key, dataset_key = self.get_index_dataset_key(channel)
+            if index_key not in self.h5_file or dataset_key not in self.h5_file:
+                invalid_channels.append(channel)
+
+        if invalid_channels:
+            raise InvalidFileError(invalid_channels)
+
+    @property
+    def df(self) -> pd.DataFrame:
+        """
+        Joins the 'per_electron', 'per_pulse', and 'per_train' using concat operation,
+        returning a single dataframe.
+
+        Returns:
+            pd.DataFrame: The combined pandas DataFrame.
+        """
+        logger.debug("Creating combined DataFrame")
+        self.validate_channel_keys()
+
+        df = pd.concat((self.df_electron, self.df_pulse, self.df_train), axis=1).sort_index()
+        logger.debug(f"Created DataFrame with shape: {df.shape}")
+
+        # Filter negative pulse values
+        df = df[df.index.get_level_values("pulseId") >= 0]
+        logger.debug(f"Filtered DataFrame shape: {df.shape}")
+
+        return df
diff --git a/src/sed/loader/flash/instruments.py b/src/sed/loader/flash/instruments.py
new file mode 100644
index 00000000..8ef0146e
--- /dev/null
+++ b/src/sed/loader/flash/instruments.py
@@ -0,0 +1,9 @@
+from __future__ import annotations
+
+from dask import dataframe as dd
+
+
+def wespe_convert(df: dd.DataFrame, df_timed: dd.DataFrame) -> tuple[dd.DataFrame, dd.DataFrame]:
+    df
+    df_timed
+    raise NotImplementedError("This function is not implemented yet.")
diff --git a/src/sed/loader/flash/loader.py b/src/sed/loader/flash/loader.py
new file mode 100644
index 00000000..c2cf79b9
--- /dev/null
+++ b/src/sed/loader/flash/loader.py
@@ -0,0 +1,415 @@
+"""
+This module implements the flash data loader.
+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 an 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 functionality.
+"""
+from __future__ import annotations
+
+import re
+import time
+from collections.abc import Sequence
+from pathlib import Path
+
+import dask.dataframe as dd
+from natsort import natsorted
+
+from sed.core.logging import set_verbosity
+from sed.core.logging import setup_logging
+from sed.loader.base.loader import BaseLoader
+from sed.loader.flash.buffer_handler import BufferHandler
+from sed.loader.flash.instruments import wespe_convert
+from sed.loader.flash.metadata import MetadataRetriever
+
+# Configure logging
+logger = setup_logging("flash_loader")
+
+
+class FlashLoader(BaseLoader):
+    """
+    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.
+
+    Args:
+        config (dict, optional): Config dictionary. Defaults to None.
+        verbose (bool, optional): Option to print out diagnostic information.
+            Defaults to True.
+    """
+
+    __name__ = "flash"
+
+    supported_file_types = ["h5"]
+
+    def __init__(self, config: dict, verbose: bool = True) -> None:
+        """
+        Initializes the FlashLoader.
+
+        Args:
+            config (dict): Configuration dictionary.
+            verbose (bool, optional): Option to print out diagnostic information.
+        """
+        super().__init__(config=config, verbose=verbose)
+
+        set_verbosity(logger, self._verbose)
+
+        self.instrument: str = self._config["core"].get("instrument", "hextof")  # default is hextof
+        self.raw_dir: str = None
+        self.processed_dir: str = None
+
+    @property
+    def verbose(self) -> bool:
+        """Accessor to the verbosity flag.
+
+        Returns:
+            bool: Verbosity flag.
+        """
+        return self._verbose
+
+    @verbose.setter
+    def verbose(self, verbose: bool):
+        """Setter for the verbosity.
+
+        Args:
+            verbose (bool): Option to turn on verbose output. Sets loglevel to INFO.
+        """
+        self._verbose = verbose
+        set_verbosity(logger, self._verbose)
+
+    def _initialize_dirs(self) -> None:
+        """
+        Initializes the directories on Maxwell based on configuration. If paths is provided in
+        the configuration, the raw data directory and parquet data directory are taken from there.
+        Otherwise, the beamtime_id and year are used to locate the data directories.
+        The first path that has either online- or express- prefix, or the daq name is taken as the
+        raw data directory.
+
+        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
+        # Only raw_dir is necessary, processed_dir can be based on raw_dir, if not provided
+        if "paths" in self._config["core"]:
+            raw_dir = Path(self._config["core"]["paths"].get("raw", ""))
+            processed_dir = Path(
+                self._config["core"]["paths"].get("processed", raw_dir.joinpath("processed")),
+            )
+
+        else:
+            try:
+                beamtime_id = self._config["core"]["beamtime_id"]
+                year = self._config["core"]["year"]
+
+            except KeyError as exc:
+                raise ValueError(
+                    "The beamtime_id and year are required.",
+                ) from exc
+
+            beamtime_dir = Path(
+                self._config["core"]["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
+            raw_paths: list[Path] = []
+
+            for path in beamtime_dir.joinpath("raw").glob("**/*"):
+                if path.is_dir():
+                    dir_name = path.name
+                    if dir_name.startswith(("online-", "express-")):
+                        raw_paths.append(path.joinpath(self._config["dataframe"]["daq"]))
+                    elif dir_name == self._config["dataframe"]["daq"].upper():
+                        raw_paths.append(path)
+
+            if not raw_paths:
+                raise FileNotFoundError("Raw data directories not found.")
+
+            raw_dir = raw_paths[0].resolve()
+
+            processed_dir = beamtime_dir.joinpath("processed")
+
+        processed_dir.mkdir(parents=True, exist_ok=True)
+
+        self.raw_dir = str(raw_dir)
+        self.processed_dir = str(processed_dir)
+
+    @property
+    def available_runs(self) -> list[int]:
+        # Get all files in raw_dir with "run" in their names
+        files = list(Path(self.raw_dir).glob("*run*"))
+
+        # Extract run IDs from filenames
+        run_ids = set()
+        for file in files:
+            match = re.search(r"run(\d+)", file.name)
+            if match:
+                run_ids.add(int(match.group(1)))
+
+        # Return run IDs in sorted order
+        return sorted(list(run_ids))
+
+    def get_files_from_run_id(  # type: ignore[override]
+        self,
+        run_id: str | int,
+        folders: str | Sequence[str] = None,
+        extension: str = "h5",
+    ) -> 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 | int): The run identifier to locate.
+            folders (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".
+
+        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["core"]["stream_name_prefixes"]
+
+        if folders is None:
+            folders = self._config["core"]["base_folder"]
+
+        if isinstance(folders, str):
+            folders = [folders]
+
+        daq = self._config["dataframe"]["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 parse_metadata(self, token: str = None) -> dict:
+        """Uses the MetadataRetriever class to fetch metadata from scicat for each run.
+
+        Returns:
+            dict: Metadata dictionary
+            token (str, optional):: The scicat token to use for fetching metadata
+        """
+        metadata_retriever = MetadataRetriever(self._config["metadata"], token)
+        metadata = metadata_retriever.get_metadata(
+            beamtime_id=self._config["core"]["beamtime_id"],
+            runs=self.runs,
+            metadata=self.metadata,
+        )
+
+        return metadata
+
+    def get_count_rate(
+        self,
+        fids: Sequence[int] = None,  # noqa: ARG002
+        **kwds,  # noqa: ARG002
+    ):
+        return None, None
+
+    def get_elapsed_time(self, fids: Sequence[int] = None, **kwds) -> float | list[float]:  # type: ignore[override]
+        """
+        Calculates the elapsed time.
+
+        Args:
+            fids (Sequence[int]): A sequence of file IDs. Defaults to all files.
+
+        Keyword Args:
+            runs: A sequence of run IDs. Takes precedence over fids.
+            aggregate: Whether to return the sum of the elapsed times across
+                    the specified files or the elapsed time for each file. Defaults to True.
+
+        Returns:
+            float | list[float]: The elapsed time(s) in seconds.
+
+        Raises:
+            KeyError: If a file ID in fids or a run ID in 'runs' does not exist in the metadata.
+        """
+        try:
+            file_statistics = self.metadata["file_statistics"]["timed"]
+        except Exception as exc:
+            raise KeyError(
+                "File statistics missing. Use 'read_dataframe' first.",
+            ) from exc
+        time_stamp_alias = self._config["dataframe"].get("time_stamp_alias", "timeStamp")
+
+        def get_elapsed_time_from_fid(fid):
+            try:
+                fid = str(fid)  # Ensure the key is a string
+                time_stamps = file_statistics[fid]["columns"][time_stamp_alias]
+                elapsed_time = time_stamps["max"] - time_stamps["min"]
+            except KeyError as exc:
+                raise KeyError(
+                    f"Timestamp metadata missing in file {fid}. "
+                    "Add timestamp column and alias to config before loading.",
+                ) from exc
+
+            return elapsed_time
+
+        def get_elapsed_time_from_run(run_id):
+            if self.raw_dir is None:
+                self._initialize_dirs()
+            files = self.get_files_from_run_id(run_id=run_id, folders=self.raw_dir)
+            fids = [self.files.index(file) for file in files]
+            return sum(get_elapsed_time_from_fid(fid) for fid in fids)
+
+        elapsed_times = []
+        runs = kwds.pop("runs", None)
+        aggregate = kwds.pop("aggregate", True)
+
+        if len(kwds) > 0:
+            raise TypeError(f"get_elapsed_time() got unexpected keyword arguments {kwds.keys()}.")
+
+        if runs is not None:
+            elapsed_times = [get_elapsed_time_from_run(run) for run in runs]
+        else:
+            if fids is None:
+                fids = range(len(self.files))
+            elapsed_times = [get_elapsed_time_from_fid(fid) for fid in fids]
+
+        if aggregate:
+            elapsed_times = sum(elapsed_times)
+
+        return elapsed_times
+
+    def read_dataframe(
+        self,
+        files: str | Sequence[str] = None,
+        folders: str | Sequence[str] = None,
+        runs: str | int | Sequence[str | int] = None,
+        ftype: str = "h5",
+        metadata: dict = {},
+        collect_metadata: bool = False,
+        **kwds,
+    ) -> tuple[dd.DataFrame, dd.DataFrame, dict]:
+        """
+        Read express data from the DAQ, generating a parquet in between.
+
+        Args:
+            files (str | Sequence[str], optional): File path(s) to process. Defaults to None.
+            folders (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 (str | int | Sequence[str | int], optional): Run identifier(s).
+                Corresponding files will be located in the location provided by ``folders``.
+                Takes precedence 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.
+
+        Keyword Args:
+            detector (str, optional): The detector to use. Defaults to "".
+            force_recreate (bool, optional): Whether to force recreation of the buffer files.
+                Defaults to False.
+            processed_dir (str, optional): The directory to save the processed files.
+                Defaults to None.
+            debug (bool, optional): Whether to run buffer creation in serial. Defaults to False.
+            remove_invalid_files (bool, optional): Whether to exclude invalid files.
+                Defaults to False.
+            token (str, optional): The scicat token to use for fetching metadata. If provided,
+                will be saved to .env file for future use. If not provided, will check environment
+                variables when collect_metadata is True.
+            filter_timed_by_electron (bool, optional): When True, the timed dataframe will only
+                contain data points where valid electron events were detected. When False, all
+                timed data points are included regardless of electron detection. Defaults to True.
+
+        Returns:
+            tuple[dd.DataFrame, dd.DataFrame, dict]: A tuple containing the concatenated DataFrame
+            and metadata.
+
+        Raises:
+            ValueError: If neither 'runs' nor 'files'/'raw_dir' is provided.
+            FileNotFoundError: If the conversion fails for some files or no data is available.
+            ValueError: If collect_metadata is True and no token is available.
+        """
+        detector = kwds.pop("detector", "")
+        force_recreate = kwds.pop("force_recreate", False)
+        processed_dir = kwds.pop("processed_dir", None)
+        debug = kwds.pop("debug", False)
+        remove_invalid_files = kwds.pop("remove_invalid_files", False)
+        token = kwds.pop("token", None)
+        filter_timed_by_electron = kwds.pop("filter_timed_by_electron", True)
+
+        if len(kwds) > 0:
+            raise ValueError(f"Unexpected keyword arguments: {kwds.keys()}")
+        t0 = time.time()
+
+        self._initialize_dirs()
+        # Prepare a list of names for the runs to read and parquets to write
+        if runs is not None:
+            files = []
+            runs_ = [str(runs)] if isinstance(runs, (str, int)) else list(map(str, runs))
+            for run in runs_:
+                run_files = self.get_files_from_run_id(
+                    run_id=run,
+                    folders=self.raw_dir,
+                )
+                files.extend(run_files)
+            self.runs = runs_
+            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.
+            super().read_dataframe(
+                files=files,
+                folders=folders,
+                ftype=ftype,
+                metadata=metadata,
+            )
+
+        bh = BufferHandler(
+            config=self._config,
+        )
+
+        # if processed_dir is None, use self.processed_dir
+        processed_dir = processed_dir or self.processed_dir
+        processed_dir = Path(processed_dir)
+
+        # Obtain the parquet filenames, metadata, and schema from the method
+        # which handles buffer file creation/reading
+        h5_paths = [Path(file) for file in self.files]
+        df, df_timed = bh.process_and_load_dataframe(
+            h5_paths=h5_paths,
+            folder=processed_dir,
+            force_recreate=force_recreate,
+            suffix=detector,
+            debug=debug,
+            remove_invalid_files=remove_invalid_files,
+            filter_timed_by_electron=filter_timed_by_electron,
+        )
+
+        if self.instrument == "wespe":
+            df, df_timed = wespe_convert(df, df_timed)
+
+        self.metadata.update(self.parse_metadata(token) if collect_metadata else {})
+        self.metadata.update(bh.metadata)
+
+        print(f"loading complete in {time.time() - t0: .2f} s")
+
+        return df, df_timed, self.metadata
+
+
+LOADER = FlashLoader
diff --git a/sed/loader/flash/metadata.py b/src/sed/loader/flash/metadata.py
similarity index 52%
rename from sed/loader/flash/metadata.py
rename to src/sed/loader/flash/metadata.py
index 9f23b59a..578fa9fd 100644
--- a/sed/loader/flash/metadata.py
+++ b/src/sed/loader/flash/metadata.py
@@ -2,13 +2,16 @@
 The module provides a MetadataRetriever class for retrieving metadata
 from a Scicat Instance based on beamtime and run IDs.
 """
-
-import warnings
-from typing import Dict
-from typing import Optional
+from __future__ import annotations
 
 import requests
 
+from sed.core.config import read_env_var
+from sed.core.config import save_env_var
+from sed.core.logging import setup_logging
+
+logger = setup_logging("flash_metadata_retriever")
+
 
 class MetadataRetriever:
     """
@@ -16,42 +19,51 @@ class MetadataRetriever:
     on beamtime and run IDs.
     """
 
-    def __init__(self, metadata_config: Dict, scicat_token: str = None) -> None:
+    def __init__(self, metadata_config: dict, token: str = None) -> None:
         """
         Initializes the MetadataRetriever class.
 
         Args:
-            metadata_config (dict): Takes a dict containing
-            at least url, and optionally token for the scicat instance.
-            scicat_token (str, optional): The token to use for fetching metadata.
+            metadata_config (dict): Takes a dict containing at least url for the scicat instance.
+            token (str, optional): The token to use for fetching metadata. If provided,
+                will be saved to .env file for future use.
         """
-        self.token = metadata_config.get("scicat_token", None)
-        if scicat_token:
-            self.token = scicat_token
-        self.url = metadata_config.get("scicat_url", None)
+        # Token handling
+        if token:
+            self.token = token
+            save_env_var("SCICAT_TOKEN", self.token)
+        else:
+            # Try to load token from config or .env file
+            self.token = read_env_var("SCICAT_TOKEN")
+
+        if not self.token:
+            raise ValueError(
+                "Token is required for metadata collection. Either provide a token "
+                "parameter or set the SCICAT_TOKEN environment variable.",
+            )
 
-        if not self.token or not self.url:
-            raise ValueError("No URL or token provided for fetching metadata from scicat.")
+        self.url = metadata_config.get("archiver_url")
+        if not self.url:
+            raise ValueError("No URL provided for fetching metadata from scicat.")
 
         self.headers = {
             "Content-Type": "application/json",
             "Accept": "application/json",
         }
-        self.token = metadata_config["scicat_token"]
 
     def get_metadata(
         self,
         beamtime_id: str,
         runs: list,
-        metadata: Optional[Dict] = None,
-    ) -> Dict:
+        metadata: dict = None,
+    ) -> dict:
         """
         Retrieves metadata for a given beamtime ID and list of runs.
 
         Args:
             beamtime_id (str): The ID of the beamtime.
             runs (list): A list of run IDs.
-            metadata (Dict, optional): The existing metadata dictionary.
+            metadata (dict, optional): The existing metadata dictionary.
             Defaults to None.
 
         Returns:
@@ -60,22 +72,21 @@ def get_metadata(
         Raises:
             Exception: If the request to retrieve metadata fails.
         """
-        # If metadata is not provided, initialize it as an empty dictionary
+        logger.debug(f"Fetching metadata for beamtime {beamtime_id}, runs: {runs}")
+
         if metadata is None:
             metadata = {}
 
-        # Iterate over the list of runs
         for run in runs:
             pid = f"{beamtime_id}/{run}"
-            # Retrieve metadata for each run and update the overall metadata dictionary
+            logger.debug(f"Retrieving metadata for PID: {pid}")
             metadata_run = self._get_metadata_per_run(pid)
-            metadata.update(
-                metadata_run,
-            )  # TODO: Not correct for multiple runs
+            metadata.update(metadata_run)  # TODO: Not correct for multiple runs
 
+        logger.debug(f"Retrieved metadata with {len(metadata)} entries")
         return metadata
 
-    def _get_metadata_per_run(self, pid: str) -> Dict:
+    def _get_metadata_per_run(self, pid: str) -> dict:
         """
         Retrieves metadata for a specific run based on the PID.
 
@@ -83,42 +94,51 @@ def _get_metadata_per_run(self, pid: str) -> Dict:
             pid (str): The PID of the run.
 
         Returns:
-            Dict: The retrieved metadata.
+            dict: The retrieved metadata.
 
         Raises:
             Exception: If the request to retrieve metadata fails.
         """
         headers2 = dict(self.headers)
-        headers2["Authorization"] = "Bearer {}".format(self.token)
+        headers2["Authorization"] = f"Bearer {self.token}"
 
         try:
+            logger.debug(f"Attempting to fetch metadata with new URL format for PID: {pid}")
             dataset_response = requests.get(
                 self._create_new_dataset_url(pid),
                 headers=headers2,
                 timeout=10,
             )
             dataset_response.raise_for_status()
+
             # Check if response is an empty object because wrong url for older implementation
             if not dataset_response.content:
+                logger.debug("Empty response, trying old URL format")
                 dataset_response = requests.get(
-                    self._create_old_dataset_url(pid), headers=headers2, timeout=10
+                    self._create_old_dataset_url(pid),
+                    headers=headers2,
+                    timeout=10,
                 )
             # If the dataset request is successful, return the retrieved metadata
             # as a JSON object
             return dataset_response.json()
+
         except requests.exceptions.RequestException as exception:
-            # If the request fails, raise warning
-            print(warnings.warn(f"Failed to retrieve metadata for PID {pid}: {str(exception)}"))
+            logger.warning(f"Failed to retrieve metadata for PID {pid}: {str(exception)}")
             return {}  # Return an empty dictionary for this run
 
     def _create_old_dataset_url(self, pid: str) -> str:
         return "{burl}/{url}/%2F{npid}".format(
-            burl=self.url, url="Datasets", npid=self._reformat_pid(pid)
+            burl=self.url,
+            url="Datasets",
+            npid=self._reformat_pid(pid),
         )
 
     def _create_new_dataset_url(self, pid: str) -> str:
         return "{burl}/{url}/{npid}".format(
-            burl=self.url, url="Datasets", npid=self._reformat_pid(pid)
+            burl=self.url,
+            url="Datasets",
+            npid=self._reformat_pid(pid),
         )
 
     def _reformat_pid(self, pid: str) -> str:
diff --git a/src/sed/loader/flash/utils.py b/src/sed/loader/flash/utils.py
new file mode 100644
index 00000000..85bca9a4
--- /dev/null
+++ b/src/sed/loader/flash/utils.py
@@ -0,0 +1,122 @@
+from __future__ import annotations
+
+
+# TODO: move to config
+MULTI_INDEX = ["trainId", "pulseId", "electronId"]
+PULSE_ALIAS = MULTI_INDEX[1]
+FORMATS = ["per_electron", "per_pulse", "per_train"]
+
+
+def get_channels(
+    config_dataframe: dict = {},
+    formats: str | list[str] = None,
+    index: bool = False,
+    extend_aux: bool = False,
+) -> list[str]:
+    """
+    Returns a list of channels associated with the specified format(s).
+    'all' returns all channels but 'pulseId' and 'dldAux' (if not extended).
+
+    Args:
+        config_dataframe (dict): The config dictionary containing the dataframe keys.
+        formats (str | list[str]): The desired format(s)
+        ('per_pulse', 'per_electron', 'per_train', 'all').
+        index (bool): If True, includes channels from the multiindex.
+        extend_aux (bool): If True, includes channels from the subchannels of the auxiliary channel.
+                else just includes the auxiliary channel alias.
+
+    Returns:
+        List[str]: A list of channels with the specified format(s).
+    """
+    channel_dict = config_dataframe.get("channels", {})
+    aux_alias = config_dataframe.get("aux_alias", "dldAux")
+
+    # If 'formats' is a single string, convert it to a list for uniform processing.
+    if isinstance(formats, str):
+        formats = [formats]
+
+    # If 'formats' is a string "all", gather all possible formats.
+    if formats == ["all"]:
+        channels = get_channels(
+            config_dataframe,
+            FORMATS,
+            index,
+            extend_aux,
+        )
+        return channels
+
+    channels = []
+
+    # Include channels from multi_index if 'index' is True.
+    if index:
+        channels.extend(MULTI_INDEX)
+
+    if formats:
+        # If 'formats' is a list, check if all elements are valid.
+        err_msg = (
+            "Invalid format. Please choose from 'per_electron', 'per_pulse', 'per_train', 'all'."
+        )
+        for format_ in formats:
+            if format_ not in FORMATS + ["all"]:
+                raise ValueError(err_msg)
+
+        # Get the available channels excluding 'pulseId'.
+        available_channels = list(channel_dict.keys())
+        # pulse alias is an index and should not be included in the list of channels.
+        if PULSE_ALIAS in available_channels:
+            available_channels.remove(PULSE_ALIAS)
+
+        for format_ in formats:
+            # Gather channels based on the specified format(s).
+            channels.extend(
+                key
+                for key in available_channels
+                if channel_dict[key]["format"] == format_ and key != aux_alias
+            )
+            # Include 'dldAuxChannels' if the format is 'per_train' and extend_aux is True.
+            # Otherwise, include 'dldAux'.
+            if format_ == FORMATS[2] and aux_alias in available_channels:
+                if extend_aux:
+                    channels.extend(
+                        channel_dict[aux_alias]["sub_channels"].keys(),
+                    )
+                else:
+                    channels.extend([aux_alias])
+
+    return channels
+
+
+def get_dtypes(config_dataframe: dict, df_cols: list) -> dict:
+    """Returns a dictionary of channels and their corresponding data types.
+    Currently Auxiliary channels are not included in the dtype dictionary.
+
+    Args:
+        config_dataframe (dict): The config dictionary containing the dataframe keys.
+        df_cols (list): A list of channels in the DataFrame.
+
+    Returns:
+        dict: A dictionary of channels and their corresponding data types.
+    """
+    channels_dict = config_dataframe.get("channels", {})
+    aux_alias = config_dataframe.get("aux_alias", "dldAux")
+    dtypes = {}
+    for channel in df_cols:
+        try:
+            dtypes[channel] = channels_dict[channel].get("dtype")
+        except KeyError:
+            try:
+                dtypes[channel] = channels_dict[aux_alias][channel].get("dtype")
+            except KeyError:
+                dtypes[channel] = None
+    return dtypes
+
+
+class InvalidFileError(Exception):
+    """Raised when an H5 file is invalid due to missing keys defined in the config."""
+
+    def __init__(self, invalid_channels: list[str]):
+        self.invalid_channels = invalid_channels
+        super().__init__(
+            f"Channels not in file: {', '.join(invalid_channels)}. "
+            "If you are using the loader, set 'remove_invalid_files' to True to ignore these files",
+        )
diff --git a/sed/loader/generic/__init__.py b/src/sed/loader/generic/__init__.py
similarity index 100%
rename from sed/loader/generic/__init__.py
rename to src/sed/loader/generic/__init__.py
diff --git a/sed/loader/generic/loader.py b/src/sed/loader/generic/loader.py
similarity index 81%
rename from sed/loader/generic/loader.py
rename to src/sed/loader/generic/loader.py
index fae675a7..a7112a3e 100644
--- a/sed/loader/generic/loader.py
+++ b/src/sed/loader/generic/loader.py
@@ -3,10 +3,9 @@
 Mostly ported from https://github.com/mpes-kit/mpes.
 @author: L. Rettig
 """
-from typing import List
-from typing import Sequence
-from typing import Tuple
-from typing import Union
+from __future__ import annotations
+
+from collections.abc import Sequence
 
 import dask.dataframe as ddf
 import numpy as np
@@ -20,7 +19,8 @@ class GenericLoader(BaseLoader):
 
     Args:
         config (dict, optional): Config dictionary. Defaults to None.
-        meta_handler (MetaHandler, optional): MetaHandler object. Defaults to None.
+        verbose (bool, optional): Option to print out diagnostic information.
+            Defaults to True.
     """
 
     __name__ = "generic"
@@ -29,23 +29,23 @@ class GenericLoader(BaseLoader):
 
     def read_dataframe(
         self,
-        files: Union[str, Sequence[str]] = None,
-        folders: Union[str, Sequence[str]] = None,
-        runs: Union[str, Sequence[str]] = None,
+        files: str | Sequence[str] = None,
+        folders: str | Sequence[str] = None,
+        runs: str | Sequence[str] = None,
         ftype: str = "parquet",
         metadata: dict = None,
         collect_metadata: bool = False,
         **kwds,
-    ) -> Tuple[ddf.DataFrame, ddf.DataFrame, dict]:
+    ) -> tuple[ddf.DataFrame, ddf.DataFrame, dict]:
         """Read stored files from a folder into a dataframe.
 
         Args:
-            files (Union[str, Sequence[str]], optional): File path(s) to process.
+            files (str | Sequence[str], optional): File path(s) to process.
                 Defaults to None.
-            folders (Union[str, Sequence[str]], optional): Path to folder(s) where files
+            folders (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
+            runs (str | Sequence[str], optional): Run identifier(s). Corresponding
                 files will be located in the location provided by ``folders``. Takes
                 precedence over ``files`` and ``folders``. Defaults to None.
             ftype (str, optional): File type to read ('parquet', 'json', 'csv', etc).
@@ -64,7 +64,7 @@ def read_dataframe(
             ValueError: Raised if the file type is not supported.
 
         Returns:
-            Tuple[ddf.DataFrame, dict]: Dask dataframe, timed dataframe and metadata
+            tuple[ddf.DataFrame, ddf.DataFrame, dict]: Dask dataframe, timed dataframe and metadata
             read from specified files.
         """
         # pylint: disable=duplicate-code
@@ -102,21 +102,21 @@ def read_dataframe(
     def get_files_from_run_id(
         self,
         run_id: str,  # noqa: ARG002
-        folders: Union[str, Sequence[str]] = None,  # noqa: ARG002
+        folders: str | Sequence[str] = None,  # noqa: ARG002
         extension: str = None,  # noqa: ARG002
         **kwds,  # noqa: ARG002
-    ) -> List[str]:
+    ) -> list[str]:
         """Locate the files for a given run identifier.
 
         Args:
             run_id (str): The run identifier to locate.
-            folders (Union[str, Sequence[str]], optional): The directory(ies) where the raw
+            folders (str | Sequence[str], optional): The directory(ies) where the raw
                 data is located. Defaults to None.
             extension (str, optional): The file extension. Defaults to "h5".
             kwds: Keyword arguments
 
         Return:
-            str: Path to the location of run data.
+            list[str]: Path to the location of run data.
         """
         raise NotImplementedError
 
@@ -124,7 +124,7 @@ def get_count_rate(
         self,
         fids: Sequence[int] = None,  # noqa: ARG002
         **kwds,  # noqa: ARG002
-    ) -> Tuple[np.ndarray, np.ndarray]:
+    ) -> tuple[np.ndarray, np.ndarray]:
         """Create count rate data for the files specified in ``fids``.
 
         Args:
@@ -133,7 +133,7 @@ def get_count_rate(
             kwds: Keyword arguments
 
         Return:
-            Tuple[np.ndarray, np.ndarray]: Arrays containing countrate and seconds
+            tuple[np.ndarray, np.ndarray]: Arrays containing countrate and seconds
             into the scan.
         """
         # TODO
diff --git a/sed/loader/loader_interface.py b/src/sed/loader/loader_interface.py
similarity index 86%
rename from sed/loader/loader_interface.py
rename to src/sed/loader/loader_interface.py
index 1cedb094..51d7d0e0 100644
--- a/sed/loader/loader_interface.py
+++ b/src/sed/loader/loader_interface.py
@@ -1,9 +1,10 @@
 """Interface to select a specified loader
 """
+from __future__ import annotations
+
 import glob
 import importlib.util
 import os
-from typing import List
 
 from sed.loader.base.loader import BaseLoader
 
@@ -11,12 +12,14 @@
 def get_loader(
     loader_name: str,
     config: dict = None,
+    verbose: bool = True,
 ) -> BaseLoader:
     """Helper function to get the loader object from it's given name.
 
     Args:
         loader_name (str): Name of the loader
         config (dict, optional): Configuration dictionary. Defaults to None.
+        verbose (bool, optional): Option to print out diagnostic information.
 
     Raises:
         ValueError: Raised if the loader cannot be found.
@@ -40,14 +43,14 @@ def get_loader(
     spec = importlib.util.spec_from_file_location("loader.py", path)
     module = importlib.util.module_from_spec(spec)
     spec.loader.exec_module(module)
-    return module.LOADER(config=config)
+    return module.LOADER(config=config, verbose=verbose)
 
 
-def get_names_of_all_loaders() -> List[str]:
+def get_names_of_all_loaders() -> list[str]:
     """Helper function to populate a list of all available loaders.
 
     Returns:
-        List[str]: List of all detected loader names.
+        list[str]: List of all detected loader names.
     """
     path_prefix = f"{os.path.dirname(__file__)}{os.sep}" if os.path.dirname(__file__) else ""
     files = glob.glob(os.path.join(path_prefix, "*", "loader.py"))
diff --git a/sed/loader/mirrorutil.py b/src/sed/loader/mirrorutil.py
similarity index 91%
rename from sed/loader/mirrorutil.py
rename to src/sed/loader/mirrorutil.py
index 1ad626b2..ed7d3df4 100644
--- a/sed/loader/mirrorutil.py
+++ b/src/sed/loader/mirrorutil.py
@@ -5,13 +5,15 @@
 Mostly ported from https://github.com/mpes-kit/mpes.
 @author: L. Rettig
 """
+from __future__ import annotations
+
 import errno
 import os
 import shutil
 from datetime import datetime
-from typing import List
 
 import dask as d
+import psutil
 from dask.diagnostics import ProgressBar
 
 
@@ -21,6 +23,11 @@ class CopyTool:
     Args:
         source (str): Source path for the copy tool.
         dest (str): Destination path for the copy tool.
+        **kwds:
+            - *safetyMargin*: Size in Byte to keep free. Defaults to 500 GBytes.
+            - *gid*: Group id to which file ownership will be set. Defaults to 1001.
+            - *scheduler*: Dask scheduler to use. Defaults to "threads".
+            - *ntasks*: number of cores to use for copying. Defaults to 25.
     """
 
     def __init__(
@@ -35,11 +42,16 @@ def __init__(
             "safetyMargin",
             1 * 2**30,
         )  # Default 500 GB safety margin
-        self.gid = kwds.pop("gid", 5050)
+        self.gid = kwds.pop("gid", 1001)
         self.scheduler = kwds.pop("scheduler", "threads")
 
-        # Default to 25 concurrent copy tasks
-        self.ntasks = int(kwds.pop("ntasks", 25))
+        # Default to 20 concurrent copy tasks
+        self.num_cores = kwds.pop("num_cores", 20)
+        if self.num_cores >= psutil.cpu_count():
+            self.num_cores = psutil.cpu_count() - 1
+
+        if len(kwds) > 0:
+            raise TypeError(f"CopyTool() got unexpected keyword arguments {kwds.keys()}.")
 
     def copy(
         self,
@@ -52,6 +64,7 @@ def copy(
         Args:
             source (str): source path
             force_copy (bool, optional): re-copy all files. Defaults to False.
+            **compute_kwds: Keyword arguments passed to dask.compute()
 
         Raises:
             FileNotFoundError: Raised if the source path is not found or empty.
@@ -161,7 +174,7 @@ def copy(
                 d.compute(
                     *copy_tasks,
                     scheduler=self.scheduler,
-                    num_workers=self.ntasks,
+                    num_workers=self.num_cores,
                     **compute_kwds,
                 )
             print("Copy finished!")
@@ -317,7 +330,7 @@ def get_target_dir(
 
 
 # replacement for os.makedirs, which is independent of umask
-def mymakedirs(path: str, mode: int, gid: int) -> List[str]:
+def mymakedirs(path: str, mode: int, gid: int) -> list[str]:
     """Creates a directory path iteratively from its root
 
     Args:
@@ -326,7 +339,7 @@ def mymakedirs(path: str, mode: int, gid: int) -> List[str]:
         gid (int): Group id of created directories
 
     Returns:
-        str: Path of created directories
+        list[str]: Path of created directories
     """
 
     if not path or os.path.exists(path):
@@ -355,7 +368,10 @@ def mycopy(source: str, dest: str, gid: int, mode: int, replace: bool = False):
     if replace:
         if os.path.exists(dest):
             os.remove(dest)
-    shutil.copy2(source, dest)
+    try:
+        shutil.copy2(source, dest)
+    except OSError:
+        shutil.copy(source, dest)
     # fix permissions and group ownership:
     os.chown(dest, -1, gid)
     os.chmod(dest, mode)
diff --git a/sed/loader/mpes/__init__.py b/src/sed/loader/mpes/__init__.py
similarity index 100%
rename from sed/loader/mpes/__init__.py
rename to src/sed/loader/mpes/__init__.py
diff --git a/sed/loader/mpes/loader.py b/src/sed/loader/mpes/loader.py
similarity index 66%
rename from sed/loader/mpes/loader.py
rename to src/sed/loader/mpes/loader.py
index b8740854..e3e75cda 100644
--- a/sed/loader/mpes/loader.py
+++ b/src/sed/loader/mpes/loader.py
@@ -3,16 +3,15 @@
 Mostly ported from https://github.com/mpes-kit/mpes.
 @author: L. Rettig
 """
+from __future__ import annotations
+
 import datetime
 import glob
 import io
 import json
 import os
-from typing import Dict
-from typing import List
-from typing import Sequence
-from typing import Tuple
-from typing import Union
+from collections.abc import Sequence
+from typing import Any
 from urllib.error import HTTPError
 from urllib.error import URLError
 from urllib.request import urlopen
@@ -25,9 +24,15 @@
 import scipy.interpolate as sint
 from natsort import natsorted
 
+from sed.core.logging import set_verbosity
+from sed.core.logging import setup_logging
 from sed.loader.base.loader import BaseLoader
 
 
+# Configure logging
+logger = setup_logging("mpes_loader")
+
+
 def load_h5_in_memory(file_path):
     """
     Load an HDF5 file entirely into memory and open it with h5py.
@@ -53,61 +58,73 @@ def load_h5_in_memory(file_path):
 
 def hdf5_to_dataframe(
     files: Sequence[str],
-    group_names: Sequence[str] = None,
-    alias_dict: Dict[str, str] = None,
+    channels: dict[str, Any] = None,
     time_stamps: bool = False,
     time_stamp_alias: str = "timeStamps",
-    ms_markers_group: str = "msMarkers",
+    ms_markers_key: str = "msMarkers",
     first_event_time_stamp_key: str = "FirstEventTimeStamp",
-    **kwds,
+    test_fid: int = 0,
 ) -> ddf.DataFrame:
     """Function to read a selection of hdf5-files, and generate a delayed dask
     dataframe from provided groups in the files. Optionally, aliases can be defined.
 
     Args:
         files (List[str]): A list of the file paths to load.
-        group_names (List[str], optional): hdf5 group names to load. Defaults to load
-            all groups containing "Stream"
-        alias_dict (Dict[str, str], optional): Dictionary of aliases for the dataframe
-            columns. Keys are the hdf5 groupnames, and values the aliases. If an alias
-            is not found, its group name is used. Defaults to read the attribute
-            "Name" from each group.
+        channels (dict[str, str], optional): hdf5 channels names to load. Each entry in the dict
+            should contain the keys "format" and "dataset_key". Defaults to load all groups
+            containing "Stream", and to read the attribute "Name" from each group.
         time_stamps (bool, optional): Option to calculate time stamps. Defaults to
             False.
         time_stamp_alias (str): Alias name for the timestamp column.
             Defaults to "timeStamps".
-        ms_markers_group (str): h5 column containing timestamp information.
+        ms_markers_key (str): hdf5 path containing timestamp information.
             Defaults to "msMarkers".
         first_event_time_stamp_key (str): h5 attribute containing the start
             timestamp of a file. Defaults to "FirstEventTimeStamp".
+        test_fid(int, optional): File ID to use for extracting shape information.
 
     Returns:
         ddf.DataFrame: The delayed Dask DataFrame
     """
-    if group_names is None:
-        group_names = []
-    if alias_dict is None:
-        alias_dict = {}
-
     # Read a file to parse the file structure
-    test_fid = kwds.pop("test_fid", 0)
     test_proc = load_h5_in_memory(files[test_fid])
-    if group_names == []:
-        group_names, alias_dict = get_groups_and_aliases(
+
+    if channels is None:
+        channels = get_datasets_and_aliases(
             h5file=test_proc,
             search_pattern="Stream",
         )
 
-    column_names = [alias_dict.get(group, group) for group in group_names]
+    electron_channels = []
+    column_names = []
+    for name, channel in channels.items():
+        if channel["format"] == "per_electron":
+            if channel["dataset_key"] in test_proc:
+                electron_channels.append(channel)
+                column_names.append(name)
+            else:
+                logger.warning(
+                    f"Entry \"{channel['dataset_key']}\" for channel \"{name}\" not found. "
+                    "Skipping the channel.",
+                )
+        elif channel["format"] != "per_file":
+            error_msg = f"Invalid 'format':{channel['format']} for channel {name}."
+            logger.error(error_msg)
+            raise ValueError(error_msg)
+
+    if not electron_channels:
+        error_msg = "No valid 'per_electron' channels found."
+        logger.error(error_msg)
+        raise ValueError(error_msg)
 
     if time_stamps:
         column_names.append(time_stamp_alias)
 
     test_array = hdf5_to_array(
         h5filename=files[test_fid],
-        group_names=group_names,
+        channels=electron_channels,
         time_stamps=time_stamps,
-        ms_markers_group=ms_markers_group,
+        ms_markers_key=ms_markers_key,
         first_event_time_stamp_key=first_event_time_stamp_key,
     )
 
@@ -119,9 +136,9 @@ def hdf5_to_dataframe(
                 da.from_delayed(
                     dask.delayed(hdf5_to_array)(
                         h5filename=f,
-                        group_names=group_names,
+                        channels=electron_channels,
                         time_stamps=time_stamps,
-                        ms_markers_group=ms_markers_group,
+                        ms_markers_key=ms_markers_key,
                         first_event_time_stamp_key=first_event_time_stamp_key,
                     ),
                     dtype=test_array.dtype,
@@ -130,25 +147,49 @@ def hdf5_to_dataframe(
             )
         except OSError as exc:
             if "Unable to synchronously open file" in str(exc):
-                print(f"Unable to open file {f}: {str(exc)}. Most likely the file is incomplete.")
+                logger.warning(
+                    f"Unable to open file {f}: {str(exc)}. Most likely the file is incomplete.",
+                )
                 pass
 
     array_stack = da.concatenate(arrays, axis=1).T
 
+    dataframe = ddf.from_dask_array(array_stack, columns=column_names)
+
+    for name, channel in channels.items():
+        if channel["format"] == "per_file":
+            if channel["dataset_key"] in test_proc.attrs:
+                values = []
+                for f in files:
+                    try:
+                        values.append(float(get_attribute(h5py.File(f), channel["dataset_key"])))
+                    except OSError:
+                        pass
+                delayeds = [
+                    add_value(partition, name, value)
+                    for partition, value in zip(dataframe.partitions, values)
+                ]
+                dataframe = ddf.from_delayed(delayeds)
+
+            else:
+                logger.warning(
+                    f"Entry \"{channel['dataset_key']}\" for channel \"{name}\" not found. "
+                    "Skipping the channel.",
+                )
+
     test_proc.close()
 
-    return ddf.from_dask_array(array_stack, columns=column_names)
+    return dataframe
 
 
 def hdf5_to_timed_dataframe(
     files: Sequence[str],
-    group_names: Sequence[str] = None,
-    alias_dict: Dict[str, str] = None,
+    channels: dict[str, Any] = None,
     time_stamps: bool = False,
     time_stamp_alias: str = "timeStamps",
-    ms_markers_group: str = "msMarkers",
+    ms_markers_key: str = "msMarkers",
     first_event_time_stamp_key: str = "FirstEventTimeStamp",
-    **kwds,
+    test_fid: int = 0,
 ) -> ddf.DataFrame:
     """Function to read a selection of hdf5-files, and generate a delayed dask
     dataframe from provided groups in the files. Optionally, aliases can be defined.
@@ -156,48 +197,57 @@ def hdf5_to_timed_dataframe(
 
     Args:
         files (List[str]): A list of the file paths to load.
-        group_names (List[str], optional): hdf5 group names to load. Defaults to load
-            all groups containing "Stream"
-        alias_dict (Dict[str, str], optional): Dictionary of aliases for the dataframe
-            columns. Keys are the hdf5 groupnames, and values the aliases. If an alias
-            is not found, its group name is used. Defaults to read the attribute
-            "Name" from each group.
+        channels (dict[str, str], optional): hdf5 channels names to load. Each entry in the dict
+            should contain the keys "format" and "groupName". Defaults to load all groups
+            containing "Stream", and to read the attribute "Name" from each group.
         time_stamps (bool, optional): Option to calculate time stamps. Defaults to
             False.
         time_stamp_alias (str): Alias name for the timestamp column.
             Defaults to "timeStamps".
-        ms_markers_group (str): h5 column containing timestamp information.
+        ms_markers_key (str): hdf5 dataset containing timestamp information.
             Defaults to "msMarkers".
         first_event_time_stamp_key (str): h5 attribute containing the start
             timestamp of a file. Defaults to "FirstEventTimeStamp".
+        test_fid(int, optional): File ID to use for extracting shape information.
 
     Returns:
         ddf.DataFrame: The delayed Dask DataFrame
     """
-    if group_names is None:
-        group_names = []
-    if alias_dict is None:
-        alias_dict = {}
-
     # Read a file to parse the file structure
-    test_fid = kwds.pop("test_fid", 0)
     test_proc = load_h5_in_memory(files[test_fid])
-    if group_names == []:
-        group_names, alias_dict = get_groups_and_aliases(
+
+    if channels is None:
+        channels = get_datasets_and_aliases(
             h5file=test_proc,
             search_pattern="Stream",
         )
 
-    column_names = [alias_dict.get(group, group) for group in group_names]
+    electron_channels = []
+    column_names = []
+
+    for name, channel in channels.items():
+        if channel["format"] == "per_electron":
+            if channel["dataset_key"] in test_proc:
+                electron_channels.append(channel)
+                column_names.append(name)
+        elif channel["format"] != "per_file":
+            error_msg = f"Invalid 'format':{channel['format']} for channel {name}."
+            logger.error(error_msg)
+            raise ValueError(error_msg)
+
+    if not electron_channels:
+        error_msg = "No valid 'per_electron' channels found."
+        logger.error(error_msg)
+        raise ValueError(error_msg)
 
     if time_stamps:
         column_names.append(time_stamp_alias)
 
     test_array = hdf5_to_timed_array(
         h5filename=files[test_fid],
-        group_names=group_names,
+        channels=electron_channels,
         time_stamps=time_stamps,
-        ms_markers_group=ms_markers_group,
+        ms_markers_key=ms_markers_key,
         first_event_time_stamp_key=first_event_time_stamp_key,
     )
 
@@ -209,9 +259,9 @@ def hdf5_to_timed_dataframe(
                 da.from_delayed(
                     dask.delayed(hdf5_to_timed_array)(
                         h5filename=f,
-                        group_names=group_names,
+                        channels=electron_channels,
                         time_stamps=time_stamps,
-                        ms_markers_group=ms_markers_group,
+                        ms_markers_key=ms_markers_key,
                         first_event_time_stamp_key=first_event_time_stamp_key,
                     ),
                     dtype=test_array.dtype,
@@ -224,17 +274,50 @@ def hdf5_to_timed_dataframe(
 
     array_stack = da.concatenate(arrays, axis=1).T
 
+    dataframe = ddf.from_dask_array(array_stack, columns=column_names)
+
+    for name, channel in channels.items():
+        if channel["format"] == "per_file":
+            if channel["dataset_key"] in test_proc.attrs:
+                values = []
+                for f in files:
+                    try:
+                        values.append(float(get_attribute(h5py.File(f), channel["dataset_key"])))
+                    except OSError:
+                        pass
+                delayeds = [
+                    add_value(partition, name, value)
+                    for partition, value in zip(dataframe.partitions, values)
+                ]
+                dataframe = ddf.from_delayed(delayeds)
+
     test_proc.close()
 
-    return ddf.from_dask_array(array_stack, columns=column_names)
+    return dataframe
 
 
-def get_groups_and_aliases(
+@dask.delayed
+def add_value(partition: ddf.DataFrame, name: str, value: float) -> ddf.DataFrame:
+    """Dask delayed helper function to add a value to each dataframe partition
+
+    Args:
+        partition (ddf.DataFrame): Dask dataframe partition
+        name (str): Name of the column to add
+        value (float): value to add to this partition
+
+    Returns:
+        ddf.DataFrame: Dataframe partition with added column
+    """
+    partition[name] = value
+    return partition
+
+
+def get_datasets_and_aliases(
     h5file: h5py.File,
     search_pattern: str = None,
     alias_key: str = "Name",
-) -> Tuple[List[str], Dict[str, str]]:
-    """Read groups and aliases from a provided hdf5 file handle
+) -> dict[str, Any]:
+    """Read datasets and aliases from a provided hdf5 file handle
 
     Args:
         h5file (h5py.File):
@@ -245,31 +328,33 @@ def get_groups_and_aliases(
             Attribute key where aliases are stored. Defaults to "Name".
 
     Returns:
-        Tuple[List[str], Dict[str, str]]:
-            The list of groupnames and the alias dictionary parsed from the file
+        dict[str, Any]:
+        A dict of aliases and groupnames parsed from the file
     """
     # get group names:
-    group_names = list(h5file)
+    dataset_names = list(h5file)
 
     # Filter the group names
     if search_pattern is None:
-        filtered_group_names = group_names
+        filtered_dataset_names = dataset_names
     else:
-        filtered_group_names = [name for name in group_names if search_pattern in name]
+        filtered_dataset_names = [name for name in dataset_names if search_pattern in name]
 
     alias_dict = {}
-    for name in filtered_group_names:
+    for name in filtered_dataset_names:
         alias_dict[name] = get_attribute(h5file[name], alias_key)
 
-    return filtered_group_names, alias_dict
+    return {
+        alias_dict[name]: {"format": "per_electron", "dataset_key": name}
+        for name in filtered_dataset_names
+    }
 
 
 def hdf5_to_array(
     h5filename: str,
-    group_names: Sequence[str],
-    data_type: str = "float32",
+    channels: Sequence[dict[str, Any]],
     time_stamps=False,
-    ms_markers_group: str = "msMarkers",
+    ms_markers_key: str = "msMarkers",
     first_event_time_stamp_key: str = "FirstEventTimeStamp",
 ) -> np.ndarray:
     """Reads the content of the given groups in an hdf5 file, and returns a
@@ -277,10 +362,11 @@ def hdf5_to_array(
 
     Args:
         h5filename (str): hdf5 file name to read from
-        group_names (str): group names to read
-        data_type (str, optional): Data type of the output data. Defaults to "float32".
-        time_stamps (bool, optional): Option to calculate time stamps. Defaults to False.
-        ms_markers_group (str): h5 column containing timestamp information.
+        channels (Sequence[dict[str, any]]):
+            channel dicts containing group names and types to read.
+        time_stamps (bool, optional):
+            Option to calculate time stamps. Defaults to False.
+        ms_markers_group (str): hdf5 dataset containing timestamp information.
             Defaults to "msMarkers".
         first_event_time_stamp_key (str): h5 attribute containing the start
             timestamp of a file. Defaults to "FirstEventTimeStamp".
@@ -292,13 +378,19 @@ def hdf5_to_array(
     # Delayed array for loading an HDF5 file of reasonable size (e.g. < 1GB)
 
     h5file = load_h5_in_memory(h5filename)
-
     # Read out groups:
     data_list = []
-    for group in group_names:
-        g_dataset = np.asarray(h5file[group])
-        if bool(data_type):
-            g_dataset = g_dataset.astype(data_type)
+    for channel in channels:
+        if channel["format"] == "per_electron":
+            g_dataset = np.asarray(h5file[channel["dataset_key"]])
+        else:
+            raise ValueError(
+                f"Invalid 'format':{channel['format']} for channel {channel['dataset_key']}.",
+            )
+        if "dtype" in channel.keys():
+            g_dataset = g_dataset.astype(channel["dtype"])
+        else:
+            g_dataset = g_dataset.astype("float32")
         data_list.append(g_dataset)
 
     # calculate time stamps
@@ -307,7 +399,7 @@ def hdf5_to_array(
         time_stamp_data = np.zeros(len(data_list[0]))
         # the ms marker contains a list of events that occurred at full ms intervals.
         # It's monotonically increasing, and can contain duplicates
-        ms_marker = np.asarray(h5file[ms_markers_group])
+        ms_marker = np.asarray(h5file[ms_markers_key])
 
         # try to get start timestamp from "FirstEventTimeStamp" attribute
         try:
@@ -349,10 +441,9 @@ def hdf5_to_array(
 
 def hdf5_to_timed_array(
     h5filename: str,
-    group_names: Sequence[str],
-    data_type: str = "float32",
+    channels: Sequence[dict[str, Any]],
     time_stamps=False,
-    ms_markers_group: str = "msMarkers",
+    ms_markers_key: str = "msMarkers",
     first_event_time_stamp_key: str = "FirstEventTimeStamp",
 ) -> np.ndarray:
     """Reads the content of the given groups in an hdf5 file, and returns a
@@ -360,10 +451,11 @@ def hdf5_to_timed_array(
 
     Args:
         h5filename (str): hdf5 file name to read from
-        group_names (str): group names to read
-        data_type (str, optional): Data type of the output data. Defaults to "float32".
-        time_stamps (bool, optional): Option to calculate time stamps. Defaults to False.
-        ms_markers_group (str): h5 column containing timestamp information.
+        channels (Sequence[dict[str, any]]):
+            channel dicts containing group names and types to read.
+        time_stamps (bool, optional):
+            Option to calculate time stamps. Defaults to False.
+        ms_markers_group (str): hdf5 dataset containing timestamp information.
             Defaults to "msMarkers".
         first_event_time_stamp_key (str): h5 attribute containing the start
             timestamp of a file. Defaults to "FirstEventTimeStamp".
@@ -379,15 +471,21 @@ def hdf5_to_timed_array(
 
     # Read out groups:
     data_list = []
-    ms_marker = np.asarray(h5file[ms_markers_group])
-    for group in group_names:
-        g_dataset = np.asarray(h5file[group])
-        if bool(data_type):
-            g_dataset = g_dataset.astype(data_type)
-
+    ms_marker = np.asarray(h5file[ms_markers_key])
+    for channel in channels:
         timed_dataset = np.zeros_like(ms_marker)
-        for i, point in enumerate(ms_marker):
-            timed_dataset[i] = g_dataset[int(point) - 1]
+        if channel["format"] == "per_electron":
+            g_dataset = np.asarray(h5file[channel["dataset_key"]])
+            for i, point in enumerate(ms_marker):
+                timed_dataset[i] = g_dataset[int(point) - 1]
+        else:
+            raise ValueError(
+                f"Invalid 'format':{channel['format']} for channel {channel['dataset_key']}.",
+            )
+        if "dtype" in channel.keys():
+            timed_dataset = timed_dataset.astype(channel["dtype"])
+        else:
+            timed_dataset = timed_dataset.astype("float32")
 
         data_list.append(timed_dataset)
 
@@ -441,20 +539,20 @@ def get_attribute(h5group: h5py.Group, attribute: str) -> str:
 
 def get_count_rate(
     h5file: h5py.File,
-    ms_markers_group: str = "msMarkers",
-) -> Tuple[np.ndarray, np.ndarray]:
+    ms_markers_key: str = "msMarkers",
+) -> tuple[np.ndarray, np.ndarray]:
     """Create count rate in the file from the msMarker column.
 
     Args:
         h5file (h5py.File): The h5file from which to get the count rate.
-        ms_markers_group (str, optional): The hdf5 group where the millisecond markers
+        ms_markers_key (str, optional): The hdf5 path where the millisecond markers
             are stored. Defaults to "msMarkers".
 
     Returns:
-        Tuple[np.ndarray, np.ndarray]: The count rate in Hz and the seconds into the
+        tuple[np.ndarray, np.ndarray]: The count rate in Hz and the seconds into the
         scan.
     """
-    ms_markers = np.asarray(h5file[ms_markers_group])
+    ms_markers = np.asarray(h5file[ms_markers_key])
     secs = np.arange(0, len(ms_markers)) / 1000
     msmarker_spline = sint.InterpolatedUnivariateSpline(secs, ms_markers, k=1)
     rate_spline = msmarker_spline.derivative()
@@ -465,19 +563,19 @@ def get_count_rate(
 
 def get_elapsed_time(
     h5file: h5py.File,
-    ms_markers_group: str = "msMarkers",
+    ms_markers_key: str = "msMarkers",
 ) -> float:
     """Return the elapsed time in the file from the msMarkers wave
 
     Args:
         h5file (h5py.File): The h5file from which to get the count rate.
-        ms_markers_group (str, optional): The hdf5 group where the millisecond markers
+        ms_markers_key (str, optional): The hdf5 path where the millisecond markers
             are stored. Defaults to "msMarkers".
 
     Return:
         float: The acquisition time of the file in seconds.
     """
-    secs = h5file[ms_markers_group].len() / 1000
+    secs = h5file[ms_markers_key].len() / 1000
 
     return secs
 
@@ -487,7 +585,7 @@ def get_archiver_data(
     archiver_channel: str,
     ts_from: float,
     ts_to: float,
-) -> Tuple[np.ndarray, np.ndarray]:
+) -> tuple[np.ndarray, np.ndarray]:
     """Extract time stamps and corresponding data from and EPICS archiver instance
 
     Args:
@@ -497,7 +595,7 @@ def get_archiver_data(
         ts_to (float): ending time stamp of the range of interest
 
     Returns:
-        Tuple[List, List]: The extracted time stamps and corresponding data
+        tuple[np.ndarray, np.ndarray]: The extracted time stamps and corresponding data
     """
     iso_from = datetime.datetime.utcfromtimestamp(ts_from).isoformat()
     iso_to = datetime.datetime.utcfromtimestamp(ts_to).isoformat()
@@ -516,7 +614,8 @@ class MpesLoader(BaseLoader):
 
     Args:
         config (dict, optional): Config dictionary. Defaults to None.
-        meta_handler (MetaHandler, optional): MetaHandler object. Defaults to None.
+        verbose (bool, optional): Option to print out diagnostic information.
+            Defaults to True.
     """
 
     __name__ = "mpes"
@@ -526,35 +625,57 @@ class MpesLoader(BaseLoader):
     def __init__(
         self,
         config: dict = None,
+        verbose: bool = True,
     ):
-        super().__init__(config=config)
+        super().__init__(config=config, verbose=verbose)
+
+        set_verbosity(logger, self._verbose)
 
         self.read_timestamps = self._config.get("dataframe", {}).get(
             "read_timestamps",
             False,
         )
 
+    @property
+    def verbose(self) -> bool:
+        """Accessor to the verbosity flag.
+
+        Returns:
+            bool: Verbosity flag.
+        """
+        return self._verbose
+
+    @verbose.setter
+    def verbose(self, verbose: bool):
+        """Setter for the verbosity.
+
+        Args:
+            verbose (bool): Option to turn on verbose output. Sets loglevel to INFO.
+        """
+        self._verbose = verbose
+        set_verbosity(logger, self._verbose)
+
     def read_dataframe(
         self,
-        files: Union[str, Sequence[str]] = None,
-        folders: Union[str, Sequence[str]] = None,
-        runs: Union[str, Sequence[str]] = None,
+        files: str | Sequence[str] = None,
+        folders: str | Sequence[str] = None,
+        runs: str | Sequence[str] = None,
         ftype: str = "h5",
         metadata: dict = None,
         collect_metadata: bool = False,
         time_stamps: bool = False,
         **kwds,
-    ) -> Tuple[ddf.DataFrame, ddf.DataFrame, dict]:
+    ) -> tuple[ddf.DataFrame, ddf.DataFrame, dict]:
         """Read stored hdf5 files from a list or from folder and returns a dask
         dataframe and corresponding metadata.
 
         Args:
-            files (Union[str, Sequence[str]], optional): File path(s) to process.
+            files (str | Sequence[str], optional): File path(s) to process.
                 Defaults to None.
-            folders (Union[str, Sequence[str]], optional): Path to folder(s) where files
+            folders (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
+            runs (str | Sequence[str], optional): Run identifier(s). Corresponding
                 files will be located in the location provided by ``folders``. Takes
                 precedence over ``files`` and ``folders``. Defaults to None.
             ftype (str, optional): File extension to use. If a folder path is given,
@@ -568,10 +689,9 @@ def read_dataframe(
                 the dataframe from ms-Markers in the files. Defaults to False.
             **kwds: Keyword parameters.
 
-                - **hdf5_groupnames** : List of groupnames to look for in the file.
-                - **hdf5_aliases**: Dictionary of aliases for the groupnames.
+                - **channels** : Dict of channel informations.
                 - **time_stamp_alias**: Alias for the timestamp column
-                - **ms_markers_group**: Group name of the millisecond marker column.
+                - **ms_markers_key**: HDF5 path of the millisecond marker column.
                 - **first_event_time_stamp_key**: Attribute name containing the start
                   timestamp of the file.
 
@@ -582,11 +702,11 @@ def read_dataframe(
             FileNotFoundError: Raised if a file or folder is not found.
 
         Returns:
-            Tuple[ddf.DataFrame, ddf.DataFrame, dict]: Dask dataframe, timed Dask
+            tuple[ddf.DataFrame, ddf.DataFrame, dict]: Dask dataframe, timed Dask
             dataframe and metadata read from specified files.
         """
         # if runs is provided, try to locate the respective files relative to the provided folder.
-        if runs is not None:  # pylint: disable=duplicate-code
+        if runs is not None:
             files = []
             if isinstance(runs, (str, int)):
                 runs = [runs]
@@ -601,7 +721,6 @@ def read_dataframe(
                 metadata=metadata,
             )
         else:
-            # pylint: disable=duplicate-code
             super().read_dataframe(
                 files=files,
                 folders=folders,
@@ -610,13 +729,9 @@ def read_dataframe(
                 metadata=metadata,
             )
 
-        hdf5_groupnames = kwds.pop(
-            "hdf5_groupnames",
-            self._config.get("dataframe", {}).get("hdf5_groupnames", []),
-        )
-        hdf5_aliases = kwds.pop(
-            "hdf5_aliases",
-            self._config.get("dataframe", {}).get("hdf5_aliases", {}),
+        channels = kwds.pop(
+            "channels",
+            self._config.get("dataframe", {}).get("channels", None),
         )
         time_stamp_alias = kwds.pop(
             "time_stamp_alias",
@@ -625,10 +740,10 @@ def read_dataframe(
                 "timeStamps",
             ),
         )
-        ms_markers_group = kwds.pop(
-            "ms_markers_group",
+        ms_markers_key = kwds.pop(
+            "ms_markers_key",
             self._config.get("dataframe", {}).get(
-                "ms_markers_group",
+                "ms_markers_key",
                 "msMarkers",
             ),
         )
@@ -641,21 +756,19 @@ def read_dataframe(
         )
         df = hdf5_to_dataframe(
             files=self.files,
-            group_names=hdf5_groupnames,
-            alias_dict=hdf5_aliases,
+            channels=channels,
             time_stamps=time_stamps,
             time_stamp_alias=time_stamp_alias,
-            ms_markers_group=ms_markers_group,
+            ms_markers_key=ms_markers_key,
             first_event_time_stamp_key=first_event_time_stamp_key,
             **kwds,
         )
         timed_df = hdf5_to_timed_dataframe(
             files=self.files,
-            group_names=hdf5_groupnames,
-            alias_dict=hdf5_aliases,
+            channels=channels,
             time_stamps=time_stamps,
             time_stamp_alias=time_stamp_alias,
-            ms_markers_group=ms_markers_group,
+            ms_markers_key=ms_markers_key,
             first_event_time_stamp_key=first_event_time_stamp_key,
             **kwds,
         )
@@ -673,29 +786,34 @@ def read_dataframe(
     def get_files_from_run_id(
         self,
         run_id: str,
-        folders: Union[str, Sequence[str]] = None,
+        folders: str | Sequence[str] = None,
         extension: str = "h5",
-        **kwds,  # noqa: ARG002
-    ) -> List[str]:
+        **kwds,
+    ) -> list[str]:
         """Locate the files for a given run identifier.
 
         Args:
             run_id (str): The run identifier to locate.
-            folders (Union[str, Sequence[str]], optional): The directory(ies) where the raw
+            folders (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
+            kwds: Keyword arguments, not used in this loader.
 
         Return:
-            List[str]: List of file path strings to the location of run data.
+            list[str]: List of file path strings to the location of run data.
         """
+        if len(kwds) > 0:
+            raise TypeError(
+                f"get_files_from_run_id() got unexpected keyword arguments {kwds.keys()}.",
+            )
+
         if folders is None:
-            folders = self._config["core"]["paths"]["data_raw_dir"]
+            folders = str(self._config["core"]["paths"]["raw"])
 
         if isinstance(folders, str):
             folders = [folders]
 
-        files: List[str] = []
+        files: list[str] = []
         for folder in folders:
             run_files = natsorted(
                 glob.glob(
@@ -714,25 +832,44 @@ def get_files_from_run_id(
         # Return the list of found files
         return files
 
-    def get_start_and_end_time(self) -> Tuple[float, float]:
+    def get_start_and_end_time(self) -> tuple[float, float]:
         """Extract the start and end time stamps from the loaded files
 
         Returns:
-            Tuple[float, float]: A tuple containing the start and end time stamps
+            tuple[float, float]: A tuple containing the start and end time stamps
         """
         h5filename = self.files[0]
+        channels = []
+        for channel in self._config["dataframe"]["channels"].values():
+            if channel["format"] == "per_electron":
+                channels = [channel]
+                break
+        if not channels:
+            raise ValueError("No valid 'per_electron' channels found.")
         timestamps = hdf5_to_array(
             h5filename=h5filename,
-            group_names=self._config["dataframe"]["hdf5_groupnames"],
+            channels=channels,
             time_stamps=True,
         )
         ts_from = timestamps[-1][1]
         h5filename = self.files[-1]
-        timestamps = hdf5_to_array(
-            h5filename=h5filename,
-            group_names=self._config["dataframe"]["hdf5_groupnames"],
-            time_stamps=True,
-        )
+        try:
+            timestamps = hdf5_to_array(
+                h5filename=h5filename,
+                channels=channels,
+                time_stamps=True,
+            )
+        except OSError:
+            try:
+                h5filename = self.files[-2]
+                timestamps = hdf5_to_array(
+                    h5filename=h5filename,
+                    channels=channels,
+                    time_stamps=True,
+                )
+            except OSError:
+                ts_to = ts_from
+                logger.warning("Could not read end time, using start time as end time!")
         ts_to = timestamps[-1][-1]
         return (ts_from, ts_to)
 
@@ -754,9 +891,9 @@ def gather_metadata(
 
         if metadata is None:
             metadata = {}
-        print("Gathering metadata from different locations")
+        logger.info("Gathering metadata from different locations")
         # Read events in with ms time stamps
-        print("Collecting time stamps...")
+        logger.info("Collecting time stamps...")
         (ts_from, ts_to) = self.get_start_and_end_time()
 
         metadata["timing"] = {
@@ -774,7 +911,7 @@ def gather_metadata(
         if "file" not in metadata:  # If already present, the value is assumed to be a dictionary
             metadata["file"] = {}
 
-        print("Collecting file metadata...")
+        logger.info("Collecting file metadata...")
         with h5py.File(files[0], "r") as h5file:
             for key, value in h5file.attrs.items():
                 key = key.replace("VSet", "V")
@@ -784,11 +921,11 @@ def gather_metadata(
             os.path.realpath(files[0]),
         )
 
-        print("Collecting data from the EPICS archive...")
+        logger.info("Collecting data from the EPICS archive...")
         # Get metadata from Epics archive if not present already
         epics_channels = self._config["metadata"]["epics_pvs"]
 
-        start = datetime.datetime.utcfromtimestamp(ts_from).isoformat()
+        start = datetime.datetime.utcfromtimestamp(ts_from)
 
         channels_missing = set(epics_channels) - set(
             metadata["file"].keys(),
@@ -796,7 +933,7 @@ def gather_metadata(
         for channel in channels_missing:
             try:
                 _, vals = get_archiver_data(
-                    archiver_url=self._config["metadata"].get("archiver_url"),
+                    archiver_url=str(self._config["metadata"].get("archiver_url")),
                     archiver_channel=channel,
                     ts_from=ts_from,
                     ts_to=ts_to,
@@ -805,28 +942,28 @@ def gather_metadata(
 
             except IndexError:
                 metadata["file"][f"{channel}"] = np.nan
-                print(
+                logger.info(
                     f"Data for channel {channel} doesn't exist for time {start}",
                 )
             except HTTPError as exc:
-                print(
+                logger.warning(
                     f"Incorrect URL for the archive channel {channel}. "
                     "Make sure that the channel name and file start and end times are "
                     "correct.",
                 )
-                print("Error code: ", exc)
+                logger.warning(f"Error code: {exc}")
             except URLError as exc:
-                print(
+                logger.warning(
                     f"Cannot access the archive URL for channel {channel}. "
                     f"Make sure that you are within the FHI network."
                     f"Skipping over channels {channels_missing}.",
                 )
-                print("Error code: ", exc)
+                logger.warning(f"Error code: {exc}")
                 break
 
         # Determine the correct aperture_config
         stamps = sorted(
-            list(self._config["metadata"]["aperture_config"]) + [start],
+            list(self._config["metadata"]["aperture_config"].keys()) + [start],
         )
         current_index = stamps.index(start)
         timestamp = stamps[current_index - 1]  # pick last configuration before file date
@@ -856,7 +993,7 @@ def gather_metadata(
                         metadata["instrument"]["analyzer"]["fa_shape"] = key
                     break
             else:
-                print("Field aperture size not found.")
+                logger.warning("Field aperture size not found.")
 
         # get contrast aperture shape and size
         if self._config["metadata"]["ca_in_channel"] in metadata["file"]:
@@ -872,7 +1009,7 @@ def gather_metadata(
                         metadata["instrument"]["analyzer"]["ca_shape"] = key
                     break
             else:
-                print("Contrast aperture size not found.")
+                logger.warning("Contrast aperture size not found.")
 
         # Storing the lens modes corresponding to lens voltages.
         # Use lens voltages present in first lens_mode entry.
@@ -881,7 +1018,7 @@ def gather_metadata(
         ].keys()
 
         lens_volts = np.array(
-            [metadata["file"].get(f"KTOF:Lens:{lens}:V", np.NaN) for lens in lens_list],
+            [metadata["file"].get(f"KTOF:Lens:{lens}:V", np.nan) for lens in lens_list],
         )
         for mode, value in self._config["metadata"]["lens_mode_config"].items():
             lens_volts_config = np.array([value[k] for k in lens_list])
@@ -893,7 +1030,7 @@ def gather_metadata(
                 metadata["instrument"]["analyzer"]["lens_mode"] = mode
                 break
         else:
-            print(
+            logger.warning(
                 "Lens mode for given lens voltages not found. "
                 "Storing lens mode from the user, if provided.",
             )
@@ -908,13 +1045,13 @@ def gather_metadata(
                 metadata["instrument"]["analyzer"]["projection"] = "reciprocal"
                 metadata["instrument"]["analyzer"]["scheme"] = "spatial dispersive"
         except IndexError:
-            print(
+            logger.warning(
                 "Lens mode must have the form, '6kV_kmodem4.0_20VTOF_v3.sav'. "
                 "Can't determine projection. "
                 "Storing projection from the user, if provided.",
             )
         except KeyError:
-            print(
+            logger.warning(
                 "Lens mode not found. Can't determine projection. "
                 "Storing projection from the user, if provided.",
             )
@@ -925,7 +1062,7 @@ def get_count_rate(
         self,
         fids: Sequence[int] = None,
         **kwds,
-    ) -> Tuple[np.ndarray, np.ndarray]:
+    ) -> tuple[np.ndarray, np.ndarray]:
         """Create count rate from the msMarker column for the files specified in
         ``fids``.
 
@@ -934,38 +1071,44 @@ def get_count_rate(
                 include. Defaults to list of all file ids.
             kwds: Keyword arguments:
 
-                - **ms_markers_group**: Name of the hdf5 group containing the ms-markers
+                - **ms_markers_key**: HDF5 path of the ms-markers
 
         Returns:
-            Tuple[np.ndarray, np.ndarray]: Arrays containing countrate and seconds
+            tuple[np.ndarray, np.ndarray]: Arrays containing countrate and seconds
             into the scan.
         """
         if fids is None:
             fids = range(0, len(self.files))
 
-        ms_markers_group = kwds.pop(
-            "ms_markers_group",
+        ms_markers_key = kwds.pop(
+            "ms_markers_key",
             self._config.get("dataframe", {}).get(
-                "ms_markers_group",
+                "ms_markers_key",
                 "msMarkers",
             ),
         )
 
+        if len(kwds) > 0:
+            raise TypeError(f"get_count_rate() got unexpected keyword arguments {kwds.keys()}.")
+
         secs_list = []
         count_rate_list = []
         accumulated_time = 0
         for fid in fids:
             try:
                 count_rate_, secs_ = get_count_rate(
-                    load_h5_in_memory(self.files[fid]),
-                    ms_markers_group=ms_markers_group,
+                    h5py.File(self.files[fid]),
+                    ms_markers_key=ms_markers_key,
                 )
                 secs_list.append((accumulated_time + secs_).T)
                 count_rate_list.append(count_rate_.T)
                 accumulated_time += secs_[-1]
             except OSError as exc:
                 if "Unable to synchronously open file" in str(exc):
-                    print(f"Unable to open file {fid}: {str(exc)}")
+                    logger.warning(
+                        f"Unable to open file {fid}: {str(exc)}. "
+                        "Most likely the file is incomplete.",
+                    )
                     pass
 
         count_rate = np.concatenate(count_rate_list)
@@ -982,7 +1125,7 @@ def get_elapsed_time(self, fids: Sequence[int] = None, **kwds) -> float:
                 include. Defaults to list of all file ids.
             kwds: Keyword arguments:
 
-                - **ms_markers_group**: Name of the hdf5 group containing the ms-markers
+                - **ms_markers_key**: HDF5 path of the millisecond marker column.
 
         Return:
             float: The elapsed time in the files in seconds.
@@ -990,24 +1133,30 @@ def get_elapsed_time(self, fids: Sequence[int] = None, **kwds) -> float:
         if fids is None:
             fids = range(0, len(self.files))
 
-        ms_markers_group = kwds.pop(
-            "ms_markers_group",
+        ms_markers_key = kwds.pop(
+            "ms_markers_key",
             self._config.get("dataframe", {}).get(
-                "ms_markers_group",
+                "ms_markers_key",
                 "msMarkers",
             ),
         )
 
+        if len(kwds) > 0:
+            raise TypeError(f"get_elapsed_time() got unexpected keyword arguments {kwds.keys()}.")
+
         secs = 0.0
         for fid in fids:
             try:
                 secs += get_elapsed_time(
-                    load_h5_in_memory(self.files[fid]),
-                    ms_markers_group=ms_markers_group,
+                    h5py.File(self.files[fid]),
+                    ms_markers_key=ms_markers_key,
                 )
             except OSError as exc:
                 if "Unable to synchronously open file" in str(exc):
-                    print(f"Unable to open file {fid}: {str(exc)}")
+                    logger.warning(
+                        f"Unable to open file {fid}: {str(exc)}. "
+                        "Most likely the file is incomplete.",
+                    )
                     pass
 
         return secs
diff --git a/sed/loader/sxp/__init__.py b/src/sed/loader/sxp/__init__.py
similarity index 100%
rename from sed/loader/sxp/__init__.py
rename to src/sed/loader/sxp/__init__.py
diff --git a/sed/loader/sxp/loader.py b/src/sed/loader/sxp/loader.py
similarity index 89%
rename from sed/loader/sxp/loader.py
rename to src/sed/loader/sxp/loader.py
index f66ff90e..28096208 100644
--- a/sed/loader/sxp/loader.py
+++ b/src/sed/loader/sxp/loader.py
@@ -9,13 +9,12 @@
 sed functionality.
 Most of the structure is identical to the FLASH loader.
 """
+from __future__ import annotations
+
 import time
+from collections.abc import Sequence
 from functools import reduce
 from pathlib import Path
-from typing import List
-from typing import Sequence
-from typing import Tuple
-from typing import Union
 
 import dask.dataframe as dd
 import h5py
@@ -29,37 +28,66 @@
 from pandas import Series
 
 from sed.core import dfops
+from sed.core.logging import set_verbosity
+from sed.core.logging import setup_logging
 from sed.loader.base.loader import BaseLoader
 from sed.loader.utils import parse_h5_keys
 from sed.loader.utils import split_dld_time_from_sector_id
 
+# Configure logging
+logger = setup_logging("sxp_loader")
+
 
 class SXPLoader(BaseLoader):
     """
     The class generates multiindexed multidimensional pandas dataframes from the new SXP
     dataformat resolved by both macro and microbunches alongside electrons.
     Only the read_dataframe (inherited and implemented) method is accessed by other modules.
+
+    Args:
+        config (dict): Config dictionary.
+        verbose (bool, optional): Option to print out diagnostic information.
     """
 
     __name__ = "sxp"
 
     supported_file_types = ["h5"]
 
-    def __init__(self, config: dict) -> None:
-        super().__init__(config=config)
+    def __init__(self, config: dict, verbose: bool = True) -> None:
+        super().__init__(config=config, verbose=verbose)
+
+        set_verbosity(logger, self._verbose)
+
         self.multi_index = ["trainId", "pulseId", "electronId"]
         self.index_per_electron: MultiIndex = None
         self.index_per_pulse: MultiIndex = None
-        self.failed_files_error: List[str] = []
-        self.array_indices: List[List[slice]] = None
+        self.failed_files_error: list[str] = []
+        self.array_indices: list[list[slice]] = None
+        self.raw_dir: str = None
+        self.processed_dir: str = None
 
-    def initialize_paths(self) -> Tuple[List[Path], Path]:
-        """
-        Initializes the paths based on the configuration.
+    @property
+    def verbose(self) -> bool:
+        """Accessor to the verbosity flag.
 
         Returns:
-            Tuple[List[Path], Path]: A tuple containing a list of raw data directories
-            paths and the parquet data directory path.
+            bool: Verbosity flag.
+        """
+        return self._verbose
+
+    @verbose.setter
+    def verbose(self, verbose: bool):
+        """Setter for the verbosity.
+
+        Args:
+            verbose (bool): Option to turn on verbose output. Sets loglevel to INFO.
+        """
+        self._verbose = verbose
+        set_verbosity(logger, self._verbose)
+
+    def _initialize_dirs(self):
+        """
+        Initializes the paths based on the configuration.
 
         Raises:
             ValueError: If required values are missing from the configuration.
@@ -68,14 +96,14 @@ def initialize_paths(self) -> Tuple[List[Path], Path]:
         # Parses to locate the raw beamtime directory from config file
         if (
             "paths" in self._config["core"]
-            and self._config["core"]["paths"].get("data_raw_dir", "")
-            and self._config["core"]["paths"].get("data_parquet_dir", "")
+            and self._config["core"]["paths"].get("raw", "")
+            and self._config["core"]["paths"].get("processed", "")
         ):
             data_raw_dir = [
-                Path(self._config["core"]["paths"].get("data_raw_dir", "")),
+                Path(self._config["core"]["paths"].get("raw", "")),
             ]
             data_parquet_dir = Path(
-                self._config["core"]["paths"].get("data_parquet_dir", ""),
+                self._config["core"]["paths"].get("processed", ""),
             )
 
         else:
@@ -88,7 +116,7 @@ def initialize_paths(self) -> Tuple[List[Path], Path]:
                 ) from exc
 
             beamtime_dir = Path(
-                self._config["dataframe"]["beamtime_dir"][self._config["core"]["beamline"]],
+                self._config["core"]["beamtime_dir"][self._config["core"]["beamline"]],
             )
             beamtime_dir = beamtime_dir.joinpath(f"{year}/{beamtime_id}/")
 
@@ -102,35 +130,36 @@ def initialize_paths(self) -> Tuple[List[Path], Path]:
 
         data_parquet_dir.mkdir(parents=True, exist_ok=True)
 
-        return data_raw_dir, data_parquet_dir
+        self.raw_dir = data_raw_dir
+        self.processed_dir = data_parquet_dir
 
     def get_files_from_run_id(
         self,
         run_id: str,
-        folders: Union[str, Sequence[str]] = None,
+        folders: str | Sequence[str] = None,
         extension: str = "h5",
         **kwds,
-    ) -> List[str]:
+    ) -> 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
+            folders (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.
 
         Returns:
-            List[str]: A list of path strings representing the collected file names.
+            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"]
-        stream_name_postfixes = self._config["dataframe"].get("stream_name_postfixes", {})
+        stream_name_prefixes = self._config["core"]["stream_name_prefixes"]
+        stream_name_postfixes = self._config["core"].get("stream_name_postfixes", {})
 
         if isinstance(run_id, (int, np.integer)):
             run_id = str(run_id).zfill(4)
@@ -143,11 +172,16 @@ def get_files_from_run_id(
 
         daq = kwds.pop("daq", self._config.get("dataframe", {}).get("daq"))
 
+        if len(kwds) > 0:
+            raise TypeError(
+                f"get_files_from_run_id() got unexpected keyword arguments {kwds.keys()}.",
+            )
+
         stream_name_postfix = stream_name_postfixes.get(daq, "")
         # Generate the file patterns to search for in the directory
         file_pattern = f"**/{stream_name_prefixes[daq]}{run_id}{stream_name_postfix}*." + extension
 
-        files: List[Path] = []
+        files: list[Path] = []
         # Use pathlib to search for matching files in each directory
         for folder in folders:
             files.extend(
@@ -167,7 +201,7 @@ def get_files_from_run_id(
         return [str(file.resolve()) for file in files]
 
     @property
-    def available_channels(self) -> List:
+    def available_channels(self) -> list:
         """Returns the channel names that are available for use,
         excluding pulseId, defined by the json file"""
         available_channels = list(self._config["dataframe"]["channels"].keys())
@@ -175,13 +209,13 @@ def available_channels(self) -> List:
         available_channels.remove("trainId")
         return available_channels
 
-    def get_channels(self, formats: Union[str, List[str]] = "", index: bool = False) -> List[str]:
+    def get_channels(self, formats: str | list[str] = "", index: bool = False) -> list[str]:
         """
         Returns a list of channels associated with the specified format(s).
 
         Args:
-            formats (Union[str, List[str]]): The desired format(s)
-                                ('per_pulse', 'per_electron', 'per_train', 'all').
+            formats (str | list[str]): The desired format(s)
+                ('per_pulse', 'per_electron', 'per_train', 'all').
             index (bool): If True, includes channels from the multi_index.
 
         Returns:
@@ -346,7 +380,7 @@ def create_numpy_array_per_channel(
         self,
         h5_file: h5py.File,
         channel: str,
-    ) -> Tuple[Series, np.ndarray]:
+    ) -> tuple[Series, np.ndarray]:
         """
         Returns a numpy array for a given channel name for a given file.
 
@@ -355,7 +389,7 @@ def create_numpy_array_per_channel(
             channel (str): The name of the channel.
 
         Returns:
-            Tuple[Series, np.ndarray]: A tuple containing the train ID Series and the numpy array
+            tuple[Series, np.ndarray]: A tuple containing the train ID Series and the numpy array
             for the channel's data.
 
         """
@@ -515,7 +549,7 @@ def create_dataframe_per_channel(
         self,
         file_path: Path,
         channel: str,
-    ) -> Union[Series, DataFrame]:
+    ) -> Series | DataFrame:
         """
         Returns a pandas DataFrame for a given channel name from a given file.
 
@@ -528,7 +562,7 @@ def create_dataframe_per_channel(
             channel (str): The name of the channel.
 
         Returns:
-            Union[Series, DataFrame]: A pandas Series or DataFrame representing the channel's data.
+            Series | DataFrame: A pandas Series or DataFrame representing the channel's data.
 
         Raises:
             ValueError: If the channel has an undefined format.
@@ -662,13 +696,13 @@ def create_dataframe_per_file(
         # Loads h5 file and creates a dataframe
         self.reset_multi_index()  # Reset MultiIndexes for next file
         df = self.concatenate_channels(file_path)
-        df = df.dropna(subset=self._config["dataframe"].get("tof_column", "dldTimeSteps"))
+        df = df.dropna(subset=self._config["dataframe"]["columns"].get("tof", "dldTimeSteps"))
         # correct the 3 bit shift which encodes the detector ID in the 8s time
         if self._config["dataframe"].get("split_sector_id_from_dld_time", False):
-            df = split_dld_time_from_sector_id(df, config=self._config)
+            df, _ = split_dld_time_from_sector_id(df, config=self._config)
         return df
 
-    def create_buffer_file(self, h5_path: Path, parquet_path: Path) -> Union[bool, Exception]:
+    def create_buffer_file(self, h5_path: Path, parquet_path: Path) -> bool | Exception:
         """
         Converts an HDF5 file to Parquet format to create a buffer file.
 
@@ -679,6 +713,9 @@ def create_buffer_file(self, h5_path: Path, parquet_path: Path) -> Union[bool, E
             h5_path (Path): Path to the input HDF5 file.
             parquet_path (Path): Path to the output Parquet file.
 
+        Returns:
+            bool | Exception: Collected exceptions if any.
+
         Raises:
             ValueError: If an error occurs during the conversion process.
 
@@ -699,7 +736,7 @@ def buffer_file_handler(
         data_parquet_dir: Path,
         detector: str,
         force_recreate: bool,
-    ) -> Tuple[List[Path], List, List]:
+    ) -> tuple[list[Path], list, list]:
         """
         Handles the conversion of buffer files (h5 to parquet) and returns the filenames.
 
@@ -709,7 +746,7 @@ def buffer_file_handler(
             force_recreate (bool): Forces recreation of buffer files
 
         Returns:
-            Tuple[List[Path], List, List]: Three lists, one for
+            tuple[list[Path], list, list]: Three lists, one for
             parquet file paths, one for metadata and one for schema.
 
         Raises:
@@ -736,7 +773,7 @@ def buffer_file_handler(
             parquet_schemas = [pq.read_schema(file) for file in existing_parquet_filenames]
             config_schema = set(self.get_channels(formats="all", index=True))
             if self._config["dataframe"].get("split_sector_id_from_dld_time", False):
-                config_schema.add(self._config["dataframe"].get("sector_id_column", False))
+                config_schema.add(self._config["dataframe"]["columns"].get("sector_id", False))
 
             for i, schema in enumerate(parquet_schemas):
                 schema_set = set(schema.names)
@@ -806,7 +843,7 @@ def parquet_handler(
         load_parquet: bool = False,
         save_parquet: bool = False,
         force_recreate: bool = False,
-    ) -> Tuple[dd.DataFrame, dd.DataFrame]:
+    ) -> tuple[dd.DataFrame, dd.DataFrame]:
         """
         Handles loading and saving of parquet files based on the provided parameters.
 
@@ -821,7 +858,7 @@ def parquet_handler(
             save_parquet (bool, optional): Saves the entire dataframe into a parquet.
             force_recreate (bool, optional): Forces recreation of buffer file.
         Returns:
-            tuple: A tuple containing two dataframes:
+            tuple[dd.DataFrame, dd.DataFrame]: A tuple containing two dataframes:
             - dataframe_electron: Dataframe containing the loaded/augmented electron data.
             - dataframe_pulse: Dataframe containing the loaded/augmented timed data.
 
@@ -862,7 +899,7 @@ def parquet_handler(
             dataframe = dd.read_parquet(filenames, calculate_divisions=True)
 
             # Channels to fill NaN values
-            channels: List[str] = self.get_channels(["per_pulse", "per_train"])
+            channels: list[str] = self.get_channels(["per_pulse", "per_train"])
 
             overlap = min(file.num_rows for file in metadata)
 
@@ -918,31 +955,33 @@ def get_elapsed_time(self, fids=None, **kwds):  # noqa: ARG002
 
     def read_dataframe(
         self,
-        files: Union[str, Sequence[str]] = None,
-        folders: Union[str, Sequence[str]] = None,
-        runs: Union[str, Sequence[str]] = None,
+        files: str | Sequence[str] = None,
+        folders: str | Sequence[str] = None,
+        runs: str | Sequence[str] = None,
         ftype: str = "h5",
         metadata: dict = None,
         collect_metadata: bool = False,
         **kwds,
-    ) -> Tuple[dd.DataFrame, dd.DataFrame, dict]:
+    ) -> tuple[dd.DataFrame, dd.DataFrame, dict]:
         """
         Read express data from the DAQ, generating a parquet in between.
 
         Args:
-            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
+            files (str | Sequence[str], optional): File path(s) to process. Defaults to None.
+            folders (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
+            runs (str | Sequence[str], optional): Run identifier(s). Corresponding files will
                 be located in the location provided by ``folders``. Takes precedence 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.
+            **kwds: Keyword arguments passed to ``parquet_handler``.
 
         Returns:
-            Tuple[dd.DataFrame, dict]: A tuple containing the concatenated DataFrame and metadata.
+            tuple[dd.DataFrame, dd.DataFrame, dict]: A tuple containing the concatenated DataFrame,
+            timed DataFrame, and metadata.
 
         Raises:
             ValueError: If neither 'runs' nor 'files'/'data_raw_dir' is provided.
@@ -950,7 +989,7 @@ def read_dataframe(
         """
         t0 = time.time()
 
-        data_raw_dir, data_parquet_dir = self.initialize_paths()
+        self._initialize_dirs()
 
         # Prepare a list of names for the runs to read and parquets to write
         if runs is not None:
@@ -960,7 +999,7 @@ def read_dataframe(
             for run in runs:
                 run_files = self.get_files_from_run_id(
                     run_id=run,
-                    folders=[str(folder.resolve()) for folder in data_raw_dir],
+                    folders=[str(Path(folder).resolve()) for folder in self.raw_dir],
                     extension=ftype,
                     daq=self._config["dataframe"]["daq"],
                 )
@@ -978,7 +1017,7 @@ def read_dataframe(
                 metadata=metadata,
             )
 
-        df, df_timed = self.parquet_handler(data_parquet_dir, **kwds)
+        df, df_timed = self.parquet_handler(Path(self.processed_dir), **kwds)
 
         if collect_metadata:
             metadata = self.gather_metadata(
diff --git a/sed/loader/utils.py b/src/sed/loader/utils.py
similarity index 61%
rename from sed/loader/utils.py
rename to src/sed/loader/utils.py
index ab3fde3a..4f18cf0f 100644
--- a/sed/loader/utils.py
+++ b/src/sed/loader/utils.py
@@ -1,14 +1,16 @@
 """Utilities for loaders
 """
+from __future__ import annotations
+
+from collections.abc import Sequence
 from glob import glob
+from pathlib import Path
 from typing import cast
-from typing import List
-from typing import Sequence
-from typing import Union
 
 import dask.dataframe
 import numpy as np
 import pandas as pd
+import pyarrow.parquet as pq
 from h5py import File
 from h5py import Group
 from natsort import natsorted
@@ -21,7 +23,7 @@ def gather_files(
     f_end: int = None,
     f_step: int = 1,
     file_sorting: bool = True,
-) -> List[str]:
+) -> list[str]:
     """Collects and sorts files with specified extension from a given folder.
 
     Args:
@@ -37,13 +39,13 @@ def gather_files(
             Defaults to True.
 
     Returns:
-        List[str]: List of collected file names.
+        list[str]: List of collected file names.
     """
     try:
         files = glob(folder + "/*." + extension)
 
         if file_sorting:
-            files = cast(List[str], natsorted(files))
+            files = cast(list[str], natsorted(files))
 
         if f_start is not None and f_end is not None:
             files = files[slice(f_start, f_end, f_step)]
@@ -55,7 +57,7 @@ def gather_files(
     return files
 
 
-def parse_h5_keys(h5_file: File, prefix: str = "") -> List[str]:
+def parse_h5_keys(h5_file: File, prefix: str = "") -> list[str]:
     """Helper method which parses the channels present in the h5 file
     Args:
         h5_file (h5py.File): The H5 file object.
@@ -63,7 +65,7 @@ def parse_h5_keys(h5_file: File, prefix: str = "") -> List[str]:
         Defaults to an empty string.
 
     Returns:
-        List[str]: A list of channel names in the H5 file.
+        list[str]: A list of channel names in the H5 file.
 
     Raises:
         Exception: If an error occurs while parsing the keys.
@@ -144,54 +146,121 @@ def split_channel_bitwise(
 
 
 def split_dld_time_from_sector_id(
-    df: Union[pd.DataFrame, dask.dataframe.DataFrame],
+    df: pd.DataFrame | dask.dataframe.DataFrame,
     tof_column: str = None,
     sector_id_column: str = None,
     sector_id_reserved_bits: int = None,
     config: dict = None,
-) -> Union[pd.DataFrame, dask.dataframe.DataFrame]:
+) -> tuple[pd.DataFrame | dask.dataframe.DataFrame, dict]:
     """Converts the 8s time in steps to time in steps and sectorID.
 
     The 8s detector encodes the dldSectorID in the 3 least significant bits of the
     dldTimeSteps channel.
 
     Args:
-        df (Union[pd.DataFrame, dask.dataframe.DataFrame]): Dataframe to use.
+        df (pd.DataFrame | dask.dataframe.DataFrame): Dataframe to use.
         tof_column (str, optional): Name of the column containing the
-            time-of-flight steps. Defaults to config["dataframe"]["tof_column"].
+            time-of-flight steps. Defaults to config["dataframe"]["columns"]["tof"].
         sector_id_column (str, optional): Name of the column containing the
-            sectorID. Defaults to config["dataframe"]["sector_id_column"].
+            sectorID. Defaults to config["dataframe"]["columns"]["sector_id"].
         sector_id_reserved_bits (int, optional): Number of bits reserved for the
-        config (dict, optional): Configuration dictionary. Defaults to None.
+        config (dict, optional): Dataframe configuration dictionary. Defaults to None.
 
     Returns:
-        Union[pd.DataFrame, dask.dataframe.DataFrame]: Dataframe with the new columns.
+        pd.DataFrame | dask.dataframe.DataFrame: Dataframe with the new columns.
     """
     if tof_column is None:
         if config is None:
             raise ValueError("Either tof_column or config must be given.")
-        tof_column = config["dataframe"]["tof_column"]
+        tof_column = config["columns"]["tof"]
     if sector_id_column is None:
         if config is None:
             raise ValueError("Either sector_id_column or config must be given.")
-        sector_id_column = config["dataframe"]["sector_id_column"]
+        sector_id_column = config["columns"]["sector_id"]
     if sector_id_reserved_bits is None:
         if config is None:
             raise ValueError("Either sector_id_reserved_bits or config must be given.")
-        sector_id_reserved_bits = config["dataframe"].get("sector_id_reserved_bits", None)
+        sector_id_reserved_bits = config.get("sector_id_reserved_bits", None)
         if sector_id_reserved_bits is None:
             raise ValueError('No value for "sector_id_reserved_bits" found in config.')
 
     if sector_id_column in df.columns:
-        raise ValueError(
-            f"Column {sector_id_column} already in dataframe. This function is not idempotent.",
+        metadata = {"applied": False, "reason": f"Column {sector_id_column} already in dataframe"}
+    else:
+        # Split the time-of-flight column into sector ID and time-of-flight steps
+        df = split_channel_bitwise(
+            df=df,
+            input_column=tof_column,
+            output_columns=[sector_id_column, tof_column],
+            bit_mask=sector_id_reserved_bits,
+            overwrite=True,
+            types=[np.int8, np.int32],
         )
-    df = split_channel_bitwise(
-        df=df,
-        input_column=tof_column,
-        output_columns=[sector_id_column, tof_column],
-        bit_mask=sector_id_reserved_bits,
-        overwrite=True,
-        types=[np.int8, np.int32],
-    )
-    return df
+        metadata = {
+            "applied": True,
+            "tof_column": tof_column,
+            "sector_id_column": sector_id_column,
+            "sector_id_reserved_bits": sector_id_reserved_bits,
+        }
+
+    return df, {"split_dld_time_from_sector_id": metadata}
+
+
+def get_stats(meta: pq.FileMetaData) -> dict:
+    """
+    Extracts the minimum and maximum of all columns from the metadata of a Parquet file.
+
+    Args:
+        meta (pq.FileMetaData): The metadata of the Parquet file.
+
+    Returns:
+        Tuple[int, int]: The minimum and maximum timestamps.
+    """
+    min_max = {}
+    for idx, name in enumerate(meta.schema.names):
+        col = []
+        for i in range(meta.num_row_groups):
+            stats = meta.row_group(i).column(idx).statistics
+            if stats is not None:
+                if stats.min is not None:
+                    col.append(stats.min)
+                if stats.max is not None:
+                    col.append(stats.max)
+        if col:
+            min_max[name] = {"min": min(col), "max": max(col)}
+    return min_max
+
+
+def get_parquet_metadata(file_paths: list[Path]) -> dict[str, dict]:
+    """
+    Extracts and organizes metadata from a list of Parquet files.
+
+    For each file, the function reads the metadata, adds the filename,
+    and extracts the minimum and maximum timestamps.
+    "row_groups" entry is removed from FileMetaData.
+
+    Args:
+        file_paths (list[Path]): A list of paths to the Parquet files.
+
+    Returns:
+        dict[str, dict]: A dictionary file index as key and the values as metadata of each file.
+    """
+    organized_metadata = {}
+    for i, file_path in enumerate(file_paths):
+        # Read the metadata for the file
+        file_meta: pq.FileMetaData = pq.read_metadata(file_path)
+        # Convert the metadata to a dictionary
+        metadata_dict = file_meta.to_dict()
+        # Add the filename to the metadata dictionary
+        metadata_dict["filename"] = str(file_path.name)
+
+        # Get column min and max values
+        metadata_dict["columns"] = get_stats(file_meta)
+
+        # Remove "row_groups" as they contain a lot of info that is not needed
+        metadata_dict.pop("row_groups", None)
+
+        # Add the metadata dictionary to the organized_metadata dictionary
+        organized_metadata[str(i)] = metadata_dict
+
+    return organized_metadata
diff --git a/tests/calibrator/test_delay.py b/tests/calibrator/test_delay.py
index 6a848e83..744b1373 100644
--- a/tests/calibrator/test_delay.py
+++ b/tests/calibrator/test_delay.py
@@ -1,9 +1,9 @@
 """Module tests.calibrator.delay, tests for the sed.calibrator.delay file
 """
+from __future__ import annotations
+
 import os
-from importlib.util import find_spec
 from typing import Any
-from typing import Dict
 
 import dask.dataframe
 import numpy as np
@@ -14,8 +14,8 @@
 from sed.core.config import parse_config
 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"
+test_dir = os.path.join(os.path.dirname(__file__), "..")
+file = test_dir + "/data/loader/mpes/Scan0030_2.h5"
 
 
 def test_delay_parameters_from_file() -> None:
@@ -114,7 +114,7 @@ def test_delay_parameters_from_delay_range_mm() -> None:
         collect_metadata=False,
     )
     dc = DelayCalibrator(config=config)
-    calibration: Dict[str, Any] = {"delay_range_mm": (1, 15)}
+    calibration: dict[str, Any] = {"delay_range_mm": (1, 15)}
     with pytest.raises(NotImplementedError):
         dc.append_delay_axis(df, calibration=calibration)
     calibration["time0"] = 1
@@ -130,14 +130,13 @@ def test_delay_parameters_from_delay_range_mm() -> None:
 delay_stage_vals = np.linspace(0, 99, 100)
 cfg = {
     "core": {"loader": "flash"},
-    "dataframe": {"delay_column": "delay"},
+    "dataframe": {"columns": {"delay": "delay"}},
     "delay": {
         "offsets": {
             "constant": 1,
             "flip_delay_axis": True,
-            "bam": {
-                "weight": 0.001,
-                "preserve_mean": False,
+            "columns": {
+                "bam": {"weight": 0.001, "preserve_mean": False},
             },
         },
     },
@@ -167,7 +166,7 @@ def test_add_offset_from_config(df=test_dataframe) -> None:
     dc = DelayCalibrator(config=config)
     df, _ = dc.add_offsets(df.copy())
     assert "delay" in df.columns
-    assert "bam" in dc.offsets.keys()
+    assert "bam" in dc.offsets["columns"].keys()
     np.testing.assert_allclose(expected, df["delay"])
 
 
@@ -189,7 +188,7 @@ def test_add_offset_from_args(df=test_dataframe) -> None:
         columns="bam",
     )
     assert "delay" in df.columns
-    assert "bam" in dc.offsets.keys()
+    assert "bam" in dc.offsets["columns"].keys()
     expected = -np.array(
         delay_stage_vals + bam_vals * 1 + 1,
     )
@@ -200,7 +199,7 @@ def test_add_offset_from_dict(df=test_dataframe) -> None:
     """test that the timing offset is corrected for correctly from config"""
     cfg_ = cfg.copy()
     offsets = cfg["delay"]["offsets"]  # type:ignore
-    offsets["bam"].pop("weight")
+    offsets["columns"]["bam"].pop("weight")
     offsets["flip_delay_axis"] = False
     cfg_.pop("delay")
     config = parse_config(
@@ -215,5 +214,5 @@ def test_add_offset_from_dict(df=test_dataframe) -> None:
     dc = DelayCalibrator(config=config)
     df, _ = dc.add_offsets(df.copy(), offsets=offsets)
     assert "delay" in df.columns
-    assert "bam" in dc.offsets.keys()
+    assert "bam" in dc.offsets["columns"].keys()
     np.testing.assert_allclose(expected, df["delay"])
diff --git a/tests/calibrator/test_energy.py b/tests/calibrator/test_energy.py
index 697bf375..81c877cf 100644
--- a/tests/calibrator/test_energy.py
+++ b/tests/calibrator/test_energy.py
@@ -1,13 +1,13 @@
 """Module tests.calibrator.energy, tests for the sed.calibrator.energy module
 """
+from __future__ import annotations
+
 import csv
 import glob
 import itertools
 import os
 from copy import deepcopy
-from importlib.util import find_spec
 from typing import Any
-from typing import Dict
 from typing import Literal
 
 import dask.dataframe
@@ -20,9 +20,9 @@
 from sed.core.config import parse_config
 from sed.loader.loader_interface import get_loader
 
-package_dir = os.path.dirname(find_spec("sed").origin)
-df_folder = package_dir + "/../tests/data/loader/mpes/"
-folder = package_dir + "/../tests/data/calibrator/"
+test_dir = os.path.join(os.path.dirname(__file__), "..")
+df_folder = test_dir + "/data/loader/mpes/"
+folder = test_dir + "/data/calibrator/"
 files = glob.glob(df_folder + "*.h5")
 
 traces_list = []
@@ -130,6 +130,10 @@ def test_feature_extract() -> None:
         ((tof[1] - tof[0]) * np.asarray(rand) + 65000) + diff,
     )
 
+    # illegal keywords
+    with pytest.raises(TypeError):
+        ec.add_ranges(ranges=rng, ref_id=ref_id, illegal_kwd=True)
+
 
 def test_adjust_ranges() -> None:
     """Test the interactive function for adjusting the feature ranges"""
@@ -162,6 +166,10 @@ def test_adjust_ranges() -> None:
         ((tof[1] - tof[0]) * np.asarray(rand) + 65000) + diff,
     )
 
+    # illegal keywords
+    with pytest.raises(TypeError):
+        ec.adjust_ranges(ranges=rng, ref_id=ref_id, apply=True, illegal_kwd=True)
+
 
 energy_scales = ["kinetic", "binding"]
 calibration_methods = ["lmfit", "lstsq", "lsqr"]
@@ -180,7 +188,7 @@ def test_calibrate_append(energy_scale: str, calibration_method: str) -> None:
         calibration_method (str): method used for calibration
     """
     config = parse_config(
-        config={"dataframe": {"tof_binning": 2}},
+        config={"dataframe": {"tof_binning": 4}},
         folder_config={},
         user_config={},
         system_config={},
@@ -194,16 +202,14 @@ def test_calibrate_append(energy_scale: str, calibration_method: str) -> None:
     ref_id = 5
     ec.add_ranges(ranges=rng, ref_id=ref_id)
     ec.feature_extract()
-    refid = 4
     e_ref = -0.5
     calibdict = ec.calibrate(
         ref_energy=e_ref,
-        ref_id=refid,
         energy_scale=energy_scale,
         method=calibration_method,
     )
     df, metadata = ec.append_energy_axis(df)
-    assert config["dataframe"]["energy_column"] in df.columns
+    assert config["dataframe"]["columns"]["energy"] in df.columns
     axis = calibdict["axis"]
     diff = np.diff(axis)
     if energy_scale == "kinetic":
@@ -217,6 +223,15 @@ def test_calibrate_append(energy_scale: str, calibration_method: str) -> None:
             value,
         )
 
+    # illegal keywords
+    with pytest.raises(TypeError):
+        calibdict = ec.calibrate(
+            ref_energy=e_ref,
+            energy_scale=energy_scale,
+            method=calibration_method,
+            illegal_kwd=True,
+        )
+
 
 calib_types = ["fit", "poly"]
 calib_dicts = [{"d": 1, "t0": 0, "E0": 0}, {"coeffs": [1, 2, 3], "E0": 0}]
@@ -240,7 +255,7 @@ def test_append_energy_axis_from_dict_kwds(calib_type: str, calib_dict: dict) ->
     df, _, _ = loader.read_dataframe(folders=df_folder, collect_metadata=False)
     ec = EnergyCalibrator(config=config, loader=loader)
     df, metadata = ec.append_energy_axis(df, calibration=calib_dict)
-    assert config["dataframe"]["energy_column"] in df.columns
+    assert config["dataframe"]["columns"]["energy"] in df.columns
 
     for key in calib_dict:
         np.testing.assert_equal(metadata["calibration"][key], calib_dict[key])
@@ -250,7 +265,7 @@ def test_append_energy_axis_from_dict_kwds(calib_type: str, calib_dict: dict) ->
     df, _, _ = loader.read_dataframe(folders=df_folder, collect_metadata=False)
     ec = EnergyCalibrator(config=config, loader=loader)
     df, metadata = ec.append_energy_axis(df, **calib_dict)
-    assert config["dataframe"]["energy_column"] in df.columns
+    assert config["dataframe"]["columns"]["energy"] in df.columns
 
     for key in calib_dict:
         np.testing.assert_equal(metadata["calibration"][key], calib_dict[key])
@@ -278,9 +293,11 @@ def test_append_tof_ns_axis() -> None:
     """
     cfg = {
         "dataframe": {
-            "tof_column": "t",
-            "tof_ns_column": "t_ns",
-            "tof_binning": 1,
+            "columns": {
+                "tof": "t",
+                "tof_ns": "t_ns",
+            },
+            "tof_binning": 2,
             "tof_binwidth": 1e-9,
         },
     }
@@ -290,19 +307,25 @@ def test_append_tof_ns_axis() -> None:
     # from kwds
     df, _, _ = loader.read_dataframe(folders=df_folder, collect_metadata=False)
     ec = EnergyCalibrator(config=config, loader=loader)
-    df, _ = ec.append_tof_ns_axis(df, binwidth=2e-9, binning=1)
-    assert config["dataframe"]["tof_ns_column"] in df.columns
+    df, _ = ec.append_tof_ns_axis(df, binwidth=2e-9, binning=2)
+    assert config["dataframe"]["columns"]["tof_ns"] in df.columns
     np.testing.assert_allclose(df[ec.tof_column], df[ec.tof_ns_column] / 4)
 
     # from config
     df, _, _ = loader.read_dataframe(folders=df_folder, collect_metadata=False)
     ec = EnergyCalibrator(config=config, loader=loader)
     df, _ = ec.append_tof_ns_axis(df)
-    assert config["dataframe"]["tof_ns_column"] in df.columns
+    assert config["dataframe"]["columns"]["tof_ns"] in df.columns
     np.testing.assert_allclose(df[ec.tof_column], df[ec.tof_ns_column] / 2)
 
+    # illegal keywords:
+    df, _, _ = loader.read_dataframe(folders=df_folder, collect_metadata=False)
+    ec = EnergyCalibrator(config=config, loader=loader)
+    with pytest.raises(TypeError):
+        df, _ = ec.append_tof_ns_axis(df, illegal_kwd=True)
+
 
-amplitude = 2.5  # pylint: disable=invalid-name
+amplitude = 2.5
 center = (730, 730)
 sample = np.array(
     [
@@ -368,7 +391,7 @@ def test_energy_correction(correction_type: str, correction_kwd: dict) -> None:
         **correction_kwd,
     )
     df, metadata = ec.apply_energy_correction(sample_df)
-    t = df[config["dataframe"]["corrected_tof_column"]]
+    t = df[config["dataframe"]["columns"]["corrected_tof"]]
     assert t[0] == t[2]
     assert t[0] < t[1]
     assert t[3] == t[5]
@@ -391,7 +414,7 @@ def test_energy_correction(correction_type: str, correction_kwd: dict) -> None:
         config=config,
         loader=get_loader("mpes", config=config),
     )
-    correction: Dict[Any, Any] = {
+    correction: dict[Any, Any] = {
         "correction_type": correction_type,
         "amplitude": amplitude,
         "center": center,
@@ -404,7 +427,7 @@ def test_energy_correction(correction_type: str, correction_kwd: dict) -> None:
         **correction,
     )
     df, metadata = ec.apply_energy_correction(sample_df)
-    t = df[config["dataframe"]["corrected_tof_column"]]
+    t = df[config["dataframe"]["columns"]["corrected_tof"]]
     assert t[0] == t[2]
     assert t[0] < t[1]
     assert t[3] == t[5]
@@ -437,7 +460,7 @@ def test_adjust_energy_correction_raises(correction_type: str) -> None:
         config=config,
         loader=get_loader("mpes", config=config),
     )
-    correction_dict: Dict[str, Any] = {
+    correction_dict: dict[str, Any] = {
         "correction_type": correction_type,
         "amplitude": amplitude,
         "center": center,
@@ -482,7 +505,7 @@ def test_energy_correction_from_dict_kwds(correction_type: str, correction_kwd:
         config=config,
         loader=get_loader("mpes", config=config),
     )
-    correction_dict: Dict[str, Any] = {
+    correction_dict: dict[str, Any] = {
         "correction_type": correction_type,
         "amplitude": amplitude,
         "center": center,
@@ -492,7 +515,7 @@ def test_energy_correction_from_dict_kwds(correction_type: str, correction_kwd:
         sample_df,
         correction=correction_dict,
     )
-    t = df[config["dataframe"]["corrected_tof_column"]]
+    t = df[config["dataframe"]["columns"]["corrected_tof"]]
     assert t[0] == t[2]
     assert t[0] < t[1]
     assert t[3] == t[5]
@@ -512,7 +535,7 @@ def test_energy_correction_from_dict_kwds(correction_type: str, correction_kwd:
         loader=get_loader("mpes", config=config),
     )
     df, metadata = ec.apply_energy_correction(sample_df, **correction_dict)
-    t = df[config["dataframe"]["corrected_tof_column"]]
+    t = df[config["dataframe"]["columns"]["corrected_tof"]]
     assert t[0] == t[2]
     assert t[0] < t[1]
     assert t[3] == t[5]
@@ -542,7 +565,7 @@ def test_apply_energy_correction_raises(correction_type: str) -> None:
         config=config,
         loader=get_loader("mpes", config=config),
     )
-    correction_dict: Dict[str, Any] = {
+    correction_dict: dict[str, Any] = {
         "correction_type": correction_type,
         "amplitude": amplitude,
         "center": center,
@@ -563,7 +586,7 @@ def test_apply_energy_correction_raises(correction_type: str) -> None:
             sample_df,
             correction=correction_dict,
         )
-        assert config["dataframe"]["corrected_tof_column"] in df.columns
+        assert config["dataframe"]["columns"]["corrected_tof"] in df.columns
 
 
 @pytest.mark.parametrize(
@@ -581,12 +604,14 @@ def test_add_offsets_functionality(energy_scale: str) -> None:
                 },
                 "offsets": {
                     "constant": 1,
-                    "off1": {
-                        "weight": 1,
-                        "preserve_mean": True,
+                    "columns": {
+                        "off1": {
+                            "weight": 1,
+                            "preserve_mean": True,
+                        },
+                        "off2": {"weight": -1, "preserve_mean": False},
+                        "off3": {"weight": 1, "preserve_mean": False, "reduction": "mean"},
                     },
-                    "off2": {"weight": -1, "preserve_mean": False},
-                    "off3": {"weight": 1, "preserve_mean": False, "reduction": "mean"},
                 },
             },
         },
@@ -621,7 +646,7 @@ def test_add_offsets_functionality(energy_scale: str) -> None:
     exp_vals -= df["off2"] * scale_sign
     exp_vals += df["off3"].mean() * scale_sign
     np.testing.assert_allclose(res["energy"].values, exp_vals.values)
-    exp_meta: Dict[str, Any] = {}
+    exp_meta: dict[str, Any] = {}
     exp_meta["applied"] = True
     exp_meta["offsets"] = ec.offsets
     assert meta == exp_meta
@@ -655,16 +680,18 @@ def test_add_offsets_functionality(energy_scale: str) -> None:
 
 def test_add_offset_raises() -> None:
     """test if add_offset raises the correct errors"""
-    cfg_dict: Dict[str, Any] = {
+    cfg_dict: dict[str, Any] = {
         "energy": {
             "calibration": {
                 "energy_scale": "kinetic",
             },
             "offsets": {
                 "constant": 1,
-                "off1": {"weight": -1, "preserve_mean": True},
-                "off2": {"weight": -1, "preserve_mean": False},
-                "off3": {"weight": 1, "preserve_mean": False, "reduction": "mean"},
+                "columns": {
+                    "off1": {"weight": -1, "preserve_mean": True},
+                    "off2": {"weight": -1, "preserve_mean": False},
+                    "off3": {"weight": 1, "preserve_mean": False, "reduction": "mean"},
+                },
             },
         },
     }
@@ -697,27 +724,27 @@ def test_add_offset_raises() -> None:
 
     # invalid sign
     with pytest.raises(TypeError):
-        cfg = deepcopy(cfg_dict)
-        cfg["energy"]["offsets"]["off1"]["weight"] = "wrong_type"
-        config = parse_config(config=cfg, folder_config={}, user_config={}, system_config={})
+        config = parse_config(config=cfg_dict, folder_config={}, user_config={}, system_config={})
+        config["energy"]["offsets"]["columns"]["off1"]["weight"] = "wrong_type"
         ec = EnergyCalibrator(config=config, loader=get_loader("flash", config=config))
         _ = ec.add_offsets(t_df)
 
         # invalid constant
     with pytest.raises(TypeError):
-        cfg = deepcopy(cfg_dict)
-        cfg["energy"]["offsets"]["constant"] = "wrong_type"
-        config = parse_config(config=cfg, folder_config={}, user_config={}, system_config={})
+        config = parse_config(config=cfg_dict, folder_config={}, user_config={}, system_config={})
+        config["energy"]["offsets"]["constant"] = "wrong_type"
         ec = EnergyCalibrator(config=config, loader=get_loader("flash", config=config))
         _ = ec.add_offsets(t_df)
 
 
 def test_align_dld_sectors() -> None:
     """test functionality and error handling of align_dld_sectors"""
-    cfg_dict: Dict[str, Any] = {
+    cfg_dict: dict[str, Any] = {
         "dataframe": {
-            "tof_column": "dldTimeSteps",
-            "sector_id_column": "dldSectorId",
+            "columns": {
+                "tof": "dldTimeSteps",
+                "sector_id": "dldSectorId",
+            },
             "sector_delays": [-0.35, -0.25, -0.15, -0.05, 0.05, 0.15, 0.25, 0.35],
         },
     }
@@ -745,7 +772,7 @@ def test_align_dld_sectors() -> None:
     t_df = dask.dataframe.from_pandas(df.copy(), npartitions=2)
     res, meta = ec.align_dld_sectors(
         t_df,
-        tof_column=cfg_dict["dataframe"]["tof_column"],
+        tof_column=cfg_dict["dataframe"]["columns"]["tof"],
         sector_delays=cfg_dict["dataframe"]["sector_delays"],
         sector_id_column="dldSectorId",
     )
diff --git a/tests/calibrator/test_momentum.py b/tests/calibrator/test_momentum.py
index c40e75e7..dcddaa85 100644
--- a/tests/calibrator/test_momentum.py
+++ b/tests/calibrator/test_momentum.py
@@ -1,11 +1,11 @@
 """Module tests.calibrator.momentum, tests for the sed.calibrator.momentum module
 """
+from __future__ import annotations
+
 import csv
 import glob
 import os
-from importlib.util import find_spec
 from typing import Any
-from typing import Dict
 
 import numpy as np
 import pytest
@@ -15,10 +15,9 @@
 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/mpes/"
-folder = package_dir + "/../tests/data/calibrator/"
+test_dir = os.path.join(os.path.dirname(__file__), "..")
+df_folder = test_dir + "/data/loader/mpes/"
+folder = test_dir + "/data/calibrator/"
 files = glob.glob(df_folder + "*.h5")
 
 momentum_map_list = []
@@ -68,6 +67,10 @@ def test_feature_extract() -> None:
     assert len(mc.pcent) == 2
     assert len(mc.pouter_ord) == 6
 
+    # illegal keywords
+    with pytest.raises(TypeError):
+        mc.feature_extract(features=np.ndarray([1, 2]), illegal_kwd=True)
+
 
 @pytest.mark.parametrize(
     "include_center",
@@ -168,6 +171,10 @@ def test_pose_correction() -> None:
     mc.pose_adjustment(scale=1.2, xtrans=8, ytrans=7, angle=-4, apply=True)
     assert np.all(np.array([mc.cdeform_field, mc.rdeform_field]) != dfield)
 
+    # Illegal keywords:
+    with pytest.raises(TypeError):
+        mc.reset_deformation(illegal_kwd=True)
+
 
 def test_apply_correction() -> None:
     """Test the application of the distortion correction to the dataframe."""
@@ -280,8 +287,8 @@ def test_apply_correction() -> None:
     zip(transformations_list, depends_on_list),
 )
 def test_apply_registration(
-    transformations: Dict[Any, Any],
-    depends_on: Dict[Any, Any],
+    transformations: dict[Any, Any],
+    depends_on: dict[Any, Any],
 ) -> None:
     """Test the application of the distortion correction to the dataframe."""
     config = parse_config(
@@ -351,6 +358,10 @@ def test_apply_registration(
                 metadata["registration"]["center"],
             )
 
+    # illegal keywords:
+    with pytest.raises(TypeError):
+        mc.pose_adjustment(illegal_kwd=True)
+
 
 def test_momentum_calibration_equiscale() -> None:
     """Test the calibration using one point and the k-distance,
@@ -384,6 +395,10 @@ def test_momentum_calibration_equiscale() -> None:
     for key, value in mc.calibration.items():
         np.testing.assert_equal(metadata["calibration"][key], value)
 
+    # illegal keywords:
+    with pytest.raises(TypeError):
+        mc.append_k_axis(df, illegal_kwd=True)
+
 
 def test_momentum_calibration_two_points() -> None:
     """Test the calibration using two k-points, and application to the dataframe."""
@@ -422,6 +437,10 @@ def test_momentum_calibration_two_points() -> None:
     # Test with passing calibration parameters
     calibration = mc.calibration.copy()
     calibration.pop("creation_date")
+    calibration.pop("grid")
+    calibration.pop("extent")
+    calibration.pop("kx_axis")
+    calibration.pop("ky_axis")
     df, _, _ = get_loader(loader_name="mpes", config=config).read_dataframe(
         folders=df_folder,
         collect_metadata=False,
diff --git a/tests/data/loader/flash/FLASH1_USER3_stream_2_run43878_file1_20230130T153807.1.h5 b/tests/data/loader/flash/FLASH1_USER3_stream_2_run43878_file1_20230130T153807.1.h5
index 02a04d9f..1eafbf33 100644
Binary files a/tests/data/loader/flash/FLASH1_USER3_stream_2_run43878_file1_20230130T153807.1.h5 and b/tests/data/loader/flash/FLASH1_USER3_stream_2_run43878_file1_20230130T153807.1.h5 differ
diff --git a/tests/data/loader/flash/FLASH1_USER3_stream_2_run43879_file1_20230130T153807.1.h5 b/tests/data/loader/flash/FLASH1_USER3_stream_2_run43879_file1_20230130T153807.1.h5
new file mode 100644
index 00000000..1524e5ad
Binary files /dev/null and b/tests/data/loader/flash/FLASH1_USER3_stream_2_run43879_file1_20230130T153807.1.h5 differ
diff --git a/tests/data/loader/flash/config.yaml b/tests/data/loader/flash/config.yaml
index f04f08e8..fbbcba25 100644
--- a/tests/data/loader/flash/config.yaml
+++ b/tests/data/loader/flash/config.yaml
@@ -9,14 +9,29 @@ core:
 
   # 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"
+    raw: "tests/data/loader/flash/"
+    processed: "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
+    # The prefixes of the stream names for different DAQ systems for parsing filenames
+  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/"
+
 
 dataframe:
   # The name of the DAQ system to use. Necessary to resolve the filenames/paths.
@@ -29,38 +44,29 @@ dataframe:
   # if true, removes the 3 bits reserved for dldSectorID from the dldTimeSteps column
   split_sector_id_from_dld_time: True
   sector_id_reserved_bits: 3
-  # dataframe column containing x coordinates
-  x_column: dldPosX
-  # dataframe column containing corrected x coordinates
-  corrected_x_column: "X"
-  # dataframe column containing kx coordinates
-  kx_column: "kx"
-  # dataframe column containing y coordinates
-
-  y_column: dldPosY
-  # dataframe column containing corrected y coordinates
-  corrected_y_column: "Y"
-  # dataframe column containing kx coordinates
-  ky_column: "ky"
-  # dataframe column containing time-of-flight data
-
-  tof_column: dldTimeSteps
-  # dataframe column containing time-of-flight data in ns
-  tof_ns_column: dldTime
-  # dataframe column containing corrected time-of-flight data
-  corrected_tof_column: "tm"
-
   # time length of a base time-of-flight bin in seconds
   tof_binwidth: 2.0576131995767355E-11
   # binning parameter for time-of-flight data. 2**tof_binning bins per base bin
   tof_binning: 3  # power of 2, 4 means 8 bins per step
-  # dataframe column containing sector ID. obtained from dldTimeSteps column
-  sector_id_column: dldSectorID
 
   sector_delays: [0., 0., 0., 0., 0., 0., 0., 0.]
 
   jitter_cols: ["dldPosX", "dldPosY", "dldTimeSteps"]
-
+  columns:
+    x: dldPosX
+    corrected_x: X
+    kx: kx
+    y: dldPosY
+    corrected_y: Y
+    ky: ky
+    tof: dldTimeSteps
+    tof_ns: dldTime
+    corrected_tof: tm
+    timestamp: timeStamp
+    auxiliary: dldAux
+    sector_id: dldSectorID
+    delay: delayStage
+    corrected_delay: pumpProbeTime
   units:
     dldPosX: 'step'
     dldPosY: 'step'
@@ -81,75 +87,72 @@ dataframe:
     # pulse ID is a necessary channel for using the loader.
     pulseId:
       format: per_electron
-      group_name: "/uncategorised/FLASH.EXP/HEXTOF.DAQ/DLD1/"
+      index_key: "/uncategorised/FLASH.EXP/HEXTOF.DAQ/DLD1/index"
+      dataset_key: "/uncategorised/FLASH.EXP/HEXTOF.DAQ/DLD1/value"
       slice: 2
+      dtype: uint16
 
     dldPosX:
       format: per_electron
-      group_name: "/uncategorised/FLASH.EXP/HEXTOF.DAQ/DLD1/"
+      index_key: "/uncategorised/FLASH.EXP/HEXTOF.DAQ/DLD1/index"
+      dataset_key: "/uncategorised/FLASH.EXP/HEXTOF.DAQ/DLD1/value"
       slice: 1
+      dtype: uint16
 
     dldPosY:
       format: per_electron
-      group_name: "/uncategorised/FLASH.EXP/HEXTOF.DAQ/DLD1/"
+      index_key: "/uncategorised/FLASH.EXP/HEXTOF.DAQ/DLD1/index"
+      dataset_key: "/uncategorised/FLASH.EXP/HEXTOF.DAQ/DLD1/value"
       slice: 0
+      dtype: uint16
 
     dldTimeSteps:
       format: per_electron
-      group_name: "/uncategorised/FLASH.EXP/HEXTOF.DAQ/DLD1/"
+      index_key: "/uncategorised/FLASH.EXP/HEXTOF.DAQ/DLD1/index"
+      dataset_key: "/uncategorised/FLASH.EXP/HEXTOF.DAQ/DLD1/value"
       slice: 3
+      dtype: uint32
 
     # The auxiliary channel has a special structure where the group further contains
     # a multidimensional structure so further aliases are defined below
     dldAux:
-      format: per_pulse
-      group_name: "/uncategorised/FLASH.EXP/HEXTOF.DAQ/DLD1/"
+      format: per_train
+      index_key: "/uncategorised/FLASH.EXP/HEXTOF.DAQ/DLD1/index"
+      dataset_key: "/uncategorised/FLASH.EXP/HEXTOF.DAQ/DLD1/value"
       slice: 4
-      dldAuxChannels:
-        sampleBias: 0
-        tofVoltage: 1
-        extractorVoltage: 2
-        extractorCurrent: 3
-        cryoTemperature: 4
-        sampleTemperature: 5
-        dldTimeBinSize: 15
+      sub_channels:
+        sampleBias:
+          slice: 0
+        tofVoltage:
+          slice: 1
+        extractorVoltage:
+          slice: 2
+        extractorCurrent:
+          slice: 3
+        cryoTemperature:
+          slice: 4
+        sampleTemperature:
+          slice: 5
+        dldTimeBinSize:
+          slice: 15
 
     timeStamp:
       format: per_train
-      group_name: "/uncategorised/FLASH.DIAG/TIMINGINFO/TIME1.BUNCH_FIRST_INDEX.1/"
+      index_key: "/uncategorised/FLASH.DIAG/TIMINGINFO/TIME1.BUNCH_FIRST_INDEX.1/index"
+      dataset_key: "/uncategorised/FLASH.DIAG/TIMINGINFO/TIME1.BUNCH_FIRST_INDEX.1/time"
 
     delayStage:
       format: per_train
-      group_name: "/zraw/FLASH.SYNC/LASER.LOCK.EXP/F1.PG.OSC/FMC0.MD22.1.ENCODER_POSITION.RD/dGroup/"
+      index_key: "/zraw/FLASH.SYNC/LASER.LOCK.EXP/F1.PG.OSC/FMC0.MD22.1.ENCODER_POSITION.RD/dGroup/index"
+      dataset_key: "/zraw/FLASH.SYNC/LASER.LOCK.EXP/F1.PG.OSC/FMC0.MD22.1.ENCODER_POSITION.RD/dGroup/value"
 
-    gmdTunnel:
+    pulserSignAdc:
       format: per_pulse
-      group_name: "/FL1/Photon Diagnostic/GMD/Pulse resolved energy/energy tunnel/"
+      index_key: "/FL1/Experiment/PG/SIS8300 100MHz ADC/CH6/TD/index"
+      dataset_key: "/FL1/Experiment/PG/SIS8300 100MHz ADC/CH6/TD/value"
 
-  # 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"]
+    gmdTunnel:
+      format: per_pulse
+      index_key: "/FL1/Photon Diagnostic/GMD/Pulse resolved energy/energy tunnel/index"
+      dataset_key: "/FL1/Photon Diagnostic/GMD/Pulse resolved energy/energy tunnel/value"
+      slice: 0
diff --git a/tests/data/loader/generic/config.yaml b/tests/data/loader/generic/config.yaml
index e901b4d3..e1c6163f 100644
--- a/tests/data/loader/generic/config.yaml
+++ b/tests/data/loader/generic/config.yaml
@@ -1 +1,2 @@
-test:
+core:
+  loader: generic
diff --git a/tests/data/loader/mpes/config.yaml b/tests/data/loader/mpes/config.yaml
index 877c531a..62e46ebc 100644
--- a/tests/data/loader/mpes/config.yaml
+++ b/tests/data/loader/mpes/config.yaml
@@ -1,3 +1,75 @@
 core:
   paths:
-    data_raw_dir: "tests/data/loader/mpes/"
+    raw: "tests/data/loader/mpes/"
+
+dataframe:
+  # hdf5 group name containing eventIDs occuring at every millisecond (used to calculate timestamps)
+  ms_markers_key: "msMarkers"
+  # hdf5 attribute containing the timestamp of the first event in a file
+  first_event_time_stamp_key: "FirstEventTimeStamp"
+  # Time stepping in seconds of the succesive events in the timed dataframe
+  timed_dataframe_unit_time: 0.001
+  # list of columns to apply jitter to
+  jitter_cols: ["X", "Y", "t", "ADC"]
+  # 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
+
+  columns:
+    x: X # dataframe column containing x coordinates
+    y: Y # dataframe column containing y coordinates
+    tof: t # dataframe column containing time-of-flight data
+    adc: ADC # dataframe column containing analog-to-digital data
+    bias: sampleBias # dataframe column containing bias voltage data
+    corrected_x: Xm # dataframe column containing corrected x coordinates
+    corrected_y: Ym # dataframe column containing corrected y coordinates
+    corrected_tof: tm # dataframe column containing corrected time-of-flight data
+    kx: kx # dataframe column containing kx coordinates
+    ky: ky # dataframe column containing ky coordinates
+    energy: energy # dataframe column containing energy data
+    delay: delay # dataframe column containing delay data
+    timestamp: timeStamps  # dataframe column containing timestamp data
+
+  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'
+
+  # dataframe channels and group names to read from the h5 files
+  channels:
+    # The X-channel
+    X:
+      format: per_electron
+      dataset_key: "Stream_0"
+    # The Y-channel
+    Y:
+      format: per_electron
+      dataset_key: "Stream_1"
+    # The tof-channel
+    t:
+      format: per_electron
+      dataset_key: "Stream_2"
+    # The ADC-channel
+    ADC:
+      format: per_electron
+      dataset_key: "Stream_4"
+    # The sample Bias-channel
+    sampleBias:
+      format: per_file
+      dataset_key: "KTOF:Lens:Sample:V"
diff --git a/tests/data/loader/sxp/config.yaml b/tests/data/loader/sxp/config.yaml
index cc4b48e5..2f88bc50 100644
--- a/tests/data/loader/sxp/config.yaml
+++ b/tests/data/loader/sxp/config.yaml
@@ -2,30 +2,42 @@ core:
   loader: sxp
   beamline: sxp
   paths:
-    data_raw_dir: "tests/data/loader/sxp/"
-    data_parquet_dir: "tests/data/loader/sxp/parquet"
-
-binning:
+    raw: "tests/data/loader/sxp/"
+    processed: "tests/data/loader/sxp/parquet"
   num_cores: 10
+  stream_name_prefixes:
+    DA03: "RAW-R"
+  stream_name_postfixes:
+    DA03: "-DA03-"
+
+  beamtime_dir:
+    sxp: "/GPFS/exfel/exp/SXP/"
 
 dataframe:
   ubid_offset: 0
   daq: DA03
   forward_fill_iterations: 2
-  x_column: dldPosX
-  corrected_x_column: "X"
-  kx_column: "kx"
-  y_column: dldPosY
-  corrected_y_column: "Y"
-  ky_column: "ky"
-  tof_column: dldTimeSteps
-  tof_ns_column: dldTime
-  corrected_tof_column: "tm"
-  bias_column: "sampleBias"
   tof_binwidth: 2.0576131995767355E-11 # in seconds
   tof_binning: 3
   jitter_cols: ["dldPosX", "dldPosY", "dldTimeSteps"]
 
+  # Column settings
+  columns:
+    x: dldPosX
+    corrected_x: X
+    kx: kx
+    y: dldPosY
+    corrected_y: Y
+    ky: ky
+    tof: dldTimeSteps
+    tof_ns: dldTime
+    corrected_tof: tm
+    timestamp: timeStamp
+    auxiliary: dldAux
+    sector_id: dldSectorID
+    delay: delayStage
+    corrected_delay: pumpProbeTime
+
   units:
     dldPosX: 'step'
     dldPosY: 'step'
@@ -77,11 +89,3 @@ dataframe:
       format: per_train
       dataset_key: "/CONTROL/SCS_ILH_LAS/MDL/OPTICALDELAY_PP800/actualPosition/value"
       index_key: "/INDEX/trainId"
-
-  stream_name_prefixes:
-    DA03: "RAW-R"
-  stream_name_postfixes:
-    DA03: "-DA03-"
-
-  beamtime_dir:
-    sxp: "/GPFS/exfel/exp/SXP/"
diff --git a/tests/helpers.py b/tests/helpers.py
index 09cd4dd6..007d068d 100644
--- a/tests/helpers.py
+++ b/tests/helpers.py
@@ -1,5 +1,7 @@
 """This file contains helper functions used in other tests.
 """
+from __future__ import annotations
+
 import numpy as np
 import xarray as xr
 
diff --git a/tests/loader/flash/conftest.py b/tests/loader/flash/conftest.py
new file mode 100644
index 00000000..eac22c39
--- /dev/null
+++ b/tests/loader/flash/conftest.py
@@ -0,0 +1,89 @@
+""" This module contains fixtures for the FEL module tests.
+"""
+import os
+import shutil
+from pathlib import Path
+
+import h5py
+import pytest
+
+from sed.core.config import parse_config
+
+test_dir = os.path.join(os.path.dirname(__file__), "../..")
+config_path = os.path.join(test_dir, "data/loader/flash/config.yaml")
+H5_PATH = "FLASH1_USER3_stream_2_run43878_file1_20230130T153807.1.h5"
+H5_PATHS = [H5_PATH, "FLASH1_USER3_stream_2_run43879_file1_20230130T153807.1.h5"]
+
+
+@pytest.fixture(name="config")
+def fixture_config_file() -> dict:
+    """Fixture providing a configuration file for FlashLoader tests.
+
+    Returns:
+        dict: The parsed configuration file.
+    """
+    return parse_config(config_path, folder_config={}, user_config={}, system_config={})
+
+
+@pytest.fixture(name="config_dataframe")
+def fixture_config_file_dataframe() -> dict:
+    """Fixture providing a configuration file for FlashLoader tests.
+
+    Returns:
+        dict: The parsed configuration file.
+    """
+    return parse_config(config_path, folder_config={}, user_config={}, system_config={})[
+        "dataframe"
+    ]
+
+
+@pytest.fixture(name="h5_file")
+def fixture_h5_file() -> h5py.File:
+    """Fixture providing an open h5 file.
+
+    Returns:
+        h5py.File: The open h5 file.
+    """
+    return h5py.File(os.path.join(test_dir, f"data/loader/flash/{H5_PATH}"), "r")
+
+
+@pytest.fixture(name="h5_file_copy")
+def fixture_h5_file_copy(tmp_path: Path) -> h5py.File:
+    """Fixture providing a copy of an open h5 file.
+
+    Returns:
+        h5py.File: The open h5 file copy.
+    """
+    # Create a copy of the h5 file in a temporary directory
+    original_file_path = os.path.join(test_dir, f"data/loader/flash/{H5_PATH}")
+    copy_file_path = tmp_path / "copy.h5"
+    shutil.copyfile(original_file_path, copy_file_path)
+
+    # Open the copy in 'read-write' mode and return it
+    return h5py.File(copy_file_path, "r+")
+
+
+@pytest.fixture(name="h5_file2_copy")
+def fixture_h5_file2_copy(tmp_path: Path) -> h5py.File:
+    """Fixture providing a copy of an open h5 file.
+
+    Returns:
+        h5py.File: The open h5 file copy.
+    """
+    # Create a copy of the h5 file in a temporary directory
+    original_file_path = os.path.join(test_dir, f"data/loader/flash/{H5_PATHS[1]}")
+    copy_file_path = tmp_path / "copy2.h5"
+    shutil.copyfile(original_file_path, copy_file_path)
+
+    # Open the copy in 'read-write' mode and return it
+    return h5py.File(copy_file_path, "r+")
+
+
+@pytest.fixture(name="h5_paths")
+def fixture_h5_paths() -> list[Path]:
+    """Fixture providing a list of h5 file paths.
+
+    Returns:
+        list: A list of h5 file paths.
+    """
+    return [Path(os.path.join(test_dir, f"data/loader/flash/{path}")) for path in H5_PATHS]
diff --git a/tests/loader/flash/test_buffer_handler.py b/tests/loader/flash/test_buffer_handler.py
new file mode 100644
index 00000000..3eb0e625
--- /dev/null
+++ b/tests/loader/flash/test_buffer_handler.py
@@ -0,0 +1,305 @@
+"""Test cases for the BufferHandler class in the Flash module."""
+from copy import deepcopy
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import pytest
+from h5py import File
+
+from sed.loader.flash.buffer_handler import BufferFilePaths
+from sed.loader.flash.buffer_handler import BufferHandler
+from sed.loader.flash.utils import get_channels
+from sed.loader.flash.utils import InvalidFileError
+
+
+def create_parquet_dir(config: dict, folder: str) -> Path:
+    """
+    Creates a directory for storing Parquet files based on the provided configuration
+    and folder name.
+    """
+
+    parquet_path = Path(config["core"]["paths"]["processed"])
+    parquet_path = parquet_path.joinpath(folder)
+    parquet_path.mkdir(parents=True, exist_ok=True)
+    return parquet_path
+
+
+def test_buffer_file_paths(config: dict, h5_paths: list[Path]) -> None:
+    """
+    Test the BufferFilePath's ability to identify files that need to be read and
+    manage buffer file paths using a directory structure.
+
+    This test performs several checks to ensure the BufferFilePath correctly identifies
+    which HDF5 files need to be read and properly manages the paths for saving buffer
+    files. It follows these steps:
+    1. Creates a directory structure for storing buffer files and initializes the BufferHandler.
+    2. Checks if the file_sets_to_process method populates the dict of missing file sets and
+       verify that initially, all provided files are considered missing.
+    3. Checks that the paths for saving buffer files are correctly generated.
+    4. Creates a single buffer file and reruns file_sets_to_process to ensure that the BufferHandler
+        recognizes one less missing file.
+    5. Checks if the force_recreate parameter forces the BufferHandler to consider all files
+    6. Cleans up by removing the created buffer file.
+    7. Tests the handling of suffix in buffer file names (for multidetector setups) by rerunning
+        the checks with modified file name parameters.
+    """
+    folder = create_parquet_dir(config, "get_files_to_read")
+    fp = BufferFilePaths(config, h5_paths, folder, suffix="", remove_invalid_files=False)
+
+    # check that all files are to be read
+    assert len(fp.file_sets_to_process()) == len(h5_paths)
+    print(folder)
+    # create expected paths
+    expected_buffer_electron_paths = [
+        folder / f"buffer/electron_{Path(path).stem}" for path in h5_paths
+    ]
+    expected_buffer_timed_paths = [folder / f"buffer/timed_{Path(path).stem}" for path in h5_paths]
+
+    # check that all buffer paths are correct
+    assert np.all(fp["electron"] == expected_buffer_electron_paths)
+    assert np.all(fp["timed"] == expected_buffer_timed_paths)
+
+    # create a single buffer file to check if it changes
+    path = {
+        "raw": h5_paths[0],
+        "electron": expected_buffer_electron_paths[0],
+        "timed": expected_buffer_timed_paths[0],
+    }
+    bh = BufferHandler(config)
+    bh._save_buffer_file(path)
+
+    # check again for files to read and expect one less file
+    fp = BufferFilePaths(config, h5_paths, folder, suffix="", remove_invalid_files=False)
+    # check that only one file is to be read
+    assert len(fp.file_sets_to_process()) == len(h5_paths) - 1
+
+    # check that both files are to be read if force_recreate is set to True
+    assert len(fp.file_sets_to_process(force_recreate=True)) == len(h5_paths)
+
+    # remove buffer files
+    Path(path["electron"]).unlink()
+    Path(path["timed"]).unlink()
+
+    # Test for adding a suffix
+    fp = BufferFilePaths(config, h5_paths, folder, "suffix", remove_invalid_files=False)
+
+    # expected buffer paths with prefix and suffix
+    for typ in ["electron", "timed"]:
+        expected_buffer_paths = [
+            folder / "buffer" / f"{typ}_{Path(path).stem}_suffix" for path in h5_paths
+        ]
+        assert np.all(fp[typ] == expected_buffer_paths)
+
+
+def test_buffer_schema_mismatch(config: dict, h5_paths: list[Path]) -> None:
+    """
+    Test function to verify schema mismatch handling in the FlashLoader's 'read_dataframe' method.
+
+    The test validates the error handling mechanism when the available channels do not match the
+    schema of the existing parquet files.
+
+    Test Steps:
+    - Attempt to read a dataframe after adding a new channel 'gmdTunnel2' to the configuration.
+    - Check for an expected error related to the mismatch between available channels and schema.
+    - Force recreation of dataframe with the added channel, ensuring successful dataframe
+      creation.
+    - Simulate a missing channel scenario by removing 'gmdTunnel2' from the configuration.
+    - Check for an error indicating a missing channel in the configuration.
+    - Clean up created buffer files after the test.
+    """
+    folder = create_parquet_dir(config, "schema_mismatch")
+    bh = BufferHandler(config)
+    bh.process_and_load_dataframe(h5_paths=h5_paths, folder=folder, debug=True)
+
+    # Manipulate the configuration to introduce a new channel 'gmdTunnel2'
+    config_dict = config
+    config_dict["dataframe"]["channels"]["gmdTunnel2"] = {
+        "index_key": "/FL1/Photon Diagnostic/GMD/Pulse resolved energy/energy tunnel/index",
+        "dataset_key": "/FL1/Photon Diagnostic/GMD/Pulse resolved energy/energy tunnel/value",
+        "format": "per_pulse",
+        "slice": 0,
+    }
+
+    # Reread the dataframe with the modified configuration, expecting a schema mismatch error
+    with pytest.raises(ValueError) as e:
+        bh = BufferHandler(config)
+        bh.process_and_load_dataframe(h5_paths=h5_paths, folder=folder, debug=True)
+    expected_error = e.value.args[0]
+
+    # Validate the specific error messages for schema mismatch
+    assert "The available channels do not match the schema of file" in expected_error
+    assert "Missing in parquet: {'gmdTunnel2'}" in expected_error
+    assert "Please check the configuration file or set force_recreate to True." in expected_error
+
+    # Force recreation of the dataframe, including the added channel 'gmdTunnel2'
+    bh = BufferHandler(config)
+    bh.process_and_load_dataframe(h5_paths=h5_paths, folder=folder, force_recreate=True, debug=True)
+
+    # Remove 'gmdTunnel2' from the configuration to simulate a missing channel scenario
+    del config["dataframe"]["channels"]["gmdTunnel2"]
+    # also results in error but different from before
+    with pytest.raises(ValueError) as e:
+        # Attempt to read the dataframe again to check for the missing channel error
+        bh = BufferHandler(config)
+        bh.process_and_load_dataframe(h5_paths=h5_paths, folder=folder, debug=True)
+
+    expected_error = e.value.args[0]
+    # Check for the specific error message indicating a missing channel in the configuration
+    assert "Missing in config: {'gmdTunnel2'}" in expected_error
+
+    # Clean up created buffer files after the test
+    for path in bh.fp["electron"]:
+        path.unlink()
+    for path in bh.fp["timed"]:
+        path.unlink()
+
+
+def test_save_buffer_files(config: dict, h5_paths: list[Path]) -> None:
+    """
+    Test the BufferHandler's ability to save buffer files serially and in parallel.
+
+    This test ensures that the BufferHandler can run both serially and in parallel, saving the
+    output to buffer files, and then it compares the resulting DataFrames to ensure they are
+    identical. This verifies that parallel processing does not affect the integrity of the data
+    saved. After the comparison, it cleans up by removing the created buffer files.
+    """
+    folder_serial = create_parquet_dir(config, "save_buffer_files_serial")
+    bh_serial = BufferHandler(config)
+    bh_serial.process_and_load_dataframe(h5_paths, folder_serial, debug=True)
+
+    folder_parallel = create_parquet_dir(config, "save_buffer_files_parallel")
+    bh_parallel = BufferHandler(config)
+    bh_parallel.process_and_load_dataframe(h5_paths, folder_parallel)
+
+    df_serial = pd.read_parquet(folder_serial)
+    df_parallel = pd.read_parquet(folder_parallel)
+
+    pd.testing.assert_frame_equal(df_serial, df_parallel)
+
+    # remove buffer files
+    for df_type in ["electron", "timed"]:
+        for path in bh_serial.fp[df_type]:
+            path.unlink()
+        for path in bh_parallel.fp[df_type]:
+            path.unlink()
+
+
+def test_save_buffer_files_exception(
+    config: dict,
+    h5_paths: list[Path],
+    h5_file_copy: File,
+    h5_file2_copy: File,
+    tmp_path: Path,
+) -> None:
+    """Test function to verify exception handling in the BufferHandler's
+    'process_and_load_dataframe' method. The test checks for exceptions raised due to missing
+    channels in the configuration and empty datasets.
+    Test Steps:
+    - Create a directory structure for storing buffer files and initialize the BufferHandler.
+    - Check for an exception when a channel is missing in the configuration.
+    - Create an empty dataset in the HDF5 file to simulate an invalid file scenario.
+    - Check for an expected error related to the missing index dataset that invalidates the file.
+    - Check for an error when 'remove_invalid_files' is set to True and the file is invalid.
+    - Create an empty dataset in the second HDF5 file to simulate an invalid file scenario.
+    - Check for an error when 'remove_invalid_files' is set to True and the file is invalid.
+    - Check for an error when only a single file is provided, and the file is not buffered.
+    """
+    folder_parallel = create_parquet_dir(config, "save_buffer_files_exception")
+    config_ = deepcopy(config)
+
+    # check exception in case of missing channel in config
+    channel = "dldPosX"
+    del config_["dataframe"]["channels"][channel]["index_key"]
+
+    # testing exception in parallel execution
+    with pytest.raises(ValueError):
+        bh = BufferHandler(config_)
+        bh.process_and_load_dataframe(h5_paths, folder_parallel, debug=False)
+
+    # check exception message with empty dataset
+    config_ = deepcopy(config)
+    channel = "testChannel"
+    channel_index_key = "test/dataset/empty/index"
+    empty_dataset_key = "test/dataset/empty/value"
+    config_["dataframe"]["channels"][channel] = {
+        "index_key": channel_index_key,
+        "dataset_key": empty_dataset_key,
+        "format": "per_train",
+    }
+
+    # create an empty dataset
+    h5_file_copy.create_dataset(
+        name=empty_dataset_key,
+        shape=0,
+    )
+
+    # expect invalid file error because of missing index dataset that invalidates entire file
+    with pytest.raises(InvalidFileError):
+        bh = BufferHandler(config_)
+        bh.process_and_load_dataframe(
+            [tmp_path / "copy.h5"],
+            folder_parallel,
+            debug=False,
+            force_recreate=True,
+        )
+
+    # create an empty dataset
+    h5_file2_copy.create_dataset(
+        name=channel_index_key,
+        shape=0,
+    )
+    h5_file2_copy.create_dataset(
+        name=empty_dataset_key,
+        shape=0,
+    )
+
+    # if remove_invalid_files is True, the file should be removed and no error should be raised
+    bh = BufferHandler(config_)
+    try:
+        bh.process_and_load_dataframe(
+            [tmp_path / "copy.h5", tmp_path / "copy2.h5"],
+            folder_parallel,
+            debug=False,
+            force_recreate=True,
+            remove_invalid_files=True,
+        )
+    except InvalidFileError:
+        assert (
+            False
+        ), "InvalidFileError should not be raised when remove_invalid_files is set to True"
+
+    # with only a single file, the file will not be buffered so a FileNotFoundError should be raised
+    with pytest.raises(FileNotFoundError):
+        bh.process_and_load_dataframe(
+            [tmp_path / "copy.h5"],
+            folder_parallel,
+            debug=False,
+            force_recreate=True,
+            remove_invalid_files=True,
+        )
+
+
+def test_get_filled_dataframe(config: dict, h5_paths: list[Path]) -> None:
+    """Test function to verify the creation of a filled dataframe from the buffer files."""
+    folder = create_parquet_dir(config, "get_filled_dataframe")
+    bh = BufferHandler(config)
+    bh.process_and_load_dataframe(h5_paths, folder)
+
+    df = pd.read_parquet(folder)
+
+    assert np.all(list(bh.df["electron"].columns) == list(df.columns) + ["dldSectorID"])
+
+    channel_pulse = set(
+        get_channels(
+            config["dataframe"],
+            formats=["per_pulse", "per_train"],
+            index=True,
+            extend_aux=True,
+        ),
+    ) - {"electronId"}
+    assert np.all(set(bh.df["timed"].columns) == channel_pulse)
+    # remove buffer files
+    for df_type in ["electron", "timed"]:
+        for path in bh.fp[df_type]:
+            path.unlink()
diff --git a/tests/loader/flash/test_dataframe_creator.py b/tests/loader/flash/test_dataframe_creator.py
new file mode 100644
index 00000000..fe1c8f79
--- /dev/null
+++ b/tests/loader/flash/test_dataframe_creator.py
@@ -0,0 +1,277 @@
+"""Tests for DataFrameCreator functionality"""
+from pathlib import Path
+
+import h5py
+import numpy as np
+import pytest
+from pandas import DataFrame
+from pandas import Index
+from pandas import MultiIndex
+
+from sed.loader.flash.dataframe import DataFrameCreator
+from sed.loader.flash.utils import get_channels
+
+
+def test_get_index_dataset_key(config_dataframe: dict, h5_paths: list[Path]) -> None:
+    """Test the creation of the index and dataset keys for a given channel."""
+    config = config_dataframe
+    channel = "dldPosX"
+    df = DataFrameCreator(config, h5_paths[0])
+    index_key, dataset_key = df.get_index_dataset_key(channel)
+    assert index_key == config["channels"][channel]["index_key"]
+    assert dataset_key == config["channels"][channel]["dataset_key"]
+
+    # remove index_key
+    del config["channels"][channel]["index_key"]
+    with pytest.raises(ValueError):
+        df.get_index_dataset_key(channel)
+
+
+def test_get_dataset_array(config_dataframe: dict, h5_paths: list[Path]) -> None:
+    """Test the creation of a h5py dataset for a given channel."""
+
+    df = DataFrameCreator(config_dataframe, h5_paths[0])
+    channel = "dldPosX"
+
+    train_id, dset = df.get_dataset_array(channel, slice_=False)
+    # Check that the train_id and np_array have the correct shapes and types
+    assert isinstance(train_id, Index)
+    assert isinstance(dset, h5py.Dataset)
+    assert train_id.name == "trainId"
+    assert train_id.shape[0] == dset.shape[0]
+    assert dset.shape[1] == 5
+    assert dset.shape[2] == 321
+
+    train_id, dset = df.get_dataset_array(channel, slice_=True)
+    assert train_id.shape[0] == dset.shape[0]
+    assert dset.shape[1] == 321
+
+    channel = "gmdTunnel"
+    train_id, dset = df.get_dataset_array(channel, True)
+    assert train_id.shape[0] == dset.shape[0]
+    assert dset.shape[1] == 500
+
+
+def test_empty_get_dataset_array(
+    config_dataframe: dict,
+    h5_paths: list[Path],
+    h5_file_copy: h5py.File,
+) -> None:
+    """Test the method when given an empty dataset."""
+
+    channel = "gmdTunnel"
+    df = DataFrameCreator(config_dataframe, h5_paths[0])
+    train_id, dset = df.get_dataset_array(channel, slice_=False)
+
+    channel_index_key = "/FL1/Photon Diagnostic/GMD/Pulse resolved energy/energy tunnel/index"
+    # channel_dataset_key = config_dataframe["channels"][channel]["group_name"] + "value"
+    empty_dataset_key = "/FL1/Photon Diagnostic/GMD/Pulse resolved energy/energy tunnel/empty"
+    config_dataframe["channels"][channel]["index_key"] = channel_index_key
+    config_dataframe["channels"][channel]["dataset_key"] = empty_dataset_key
+
+    # create an empty dataset
+    h5_file_copy.create_dataset(
+        name=empty_dataset_key,
+        shape=(train_id.shape[0], 0),
+    )
+
+    df = DataFrameCreator(config_dataframe, h5_paths[0])
+    df.h5_file = h5_file_copy
+    train_id, dset_empty = df.get_dataset_array(channel, slice_=False)
+
+    assert dset_empty.shape[0] == train_id.shape[0]
+    assert dset.shape[1] == 8
+    assert dset_empty.shape[1] == 0
+
+
+def test_pulse_index(config_dataframe: dict, h5_paths: list[Path]) -> None:
+    """Test the creation of the pulse index for electron resolved data"""
+
+    df = DataFrameCreator(config_dataframe, h5_paths[0])
+    pulse_index, pulse_array = df.get_dataset_array("pulseId", slice_=True)
+    index, indexer = df.pulse_index(config_dataframe["ubid_offset"])
+    # Check if the index_per_electron is a MultiIndex and has the correct levels
+    assert isinstance(index, MultiIndex)
+    assert set(index.names) == {"trainId", "pulseId", "electronId"}
+
+    # Check if the pulse_index has the correct number of elements
+    # This should be the pulses without nan values
+    pulse_rav = pulse_array.ravel()
+    pulse_no_nan = pulse_rav[~np.isnan(pulse_rav)]
+    assert len(index) == len(pulse_no_nan)
+
+    # Check if all pulseIds are correctly mapped to the index
+    assert np.all(
+        index.get_level_values("pulseId").values
+        == (pulse_no_nan - config_dataframe["ubid_offset"])[indexer],
+    )
+
+    assert np.all(
+        index.get_level_values("electronId").values[:5] == [0, 1, 0, 1, 0],
+    )
+
+    assert np.all(
+        index.get_level_values("electronId").values[-5:] == [1, 0, 1, 0, 1],
+    )
+
+    # check if all indexes are unique and monotonic increasing
+    assert index.is_unique
+    assert index.is_monotonic_increasing
+
+
+def test_df_electron(config_dataframe: dict, h5_paths: list[Path]) -> None:
+    """Test the creation of a pandas DataFrame for a channel of type [per electron]."""
+    df = DataFrameCreator(config_dataframe, h5_paths[0])
+
+    result_df = df.df_electron
+
+    # check index levels
+    assert set(result_df.index.names) == {"trainId", "pulseId", "electronId"}
+
+    # check that there are no nan values in the dataframe
+    assert ~result_df.isnull().values.any()
+
+    # Check if first 5 values are as expected
+    # e.g. that the values are dropped for pulseId index below 0 (ubid_offset)
+    # however in this data the lowest value is 9 and offset was 5 so no values are dropped
+    assert np.all(
+        result_df.values[:5]
+        == np.array(
+            [
+                [556.0, 731.0, 42888.0],
+                [549.0, 737.0, 42881.0],
+                [671.0, 577.0, 39181.0],
+                [671.0, 579.0, 39196.0],
+                [714.0, 859.0, 37530.0],
+            ],
+            dtype=np.float32,
+        ),
+    )
+    assert np.all(result_df.index.get_level_values("pulseId") >= 0)
+    assert isinstance(result_df, DataFrame)
+
+    assert result_df.index.is_unique
+
+    # check that dataframe contains all subchannels
+    assert np.all(
+        set(result_df.columns) == set(get_channels(config_dataframe, ["per_electron"])),
+    )
+
+
+def test_create_dataframe_per_pulse(config_dataframe: dict, h5_paths: list[Path]) -> None:
+    """Test the creation of a pandas DataFrame for a channel of type [per pulse]."""
+    df = DataFrameCreator(config_dataframe, h5_paths[0])
+    result_df = df.df_pulse
+    # Check that the result_df is a DataFrame and has the correct shape
+    assert isinstance(result_df, DataFrame)
+
+    _, data = df.get_dataset_array("gmdTunnel", slice_=True)
+    assert result_df.shape[0] == data.shape[0] * data.shape[1]
+
+    # check index levels
+    assert set(result_df.index.names) == {"trainId", "pulseId", "electronId"}
+
+    # all electronIds should be 0
+    assert np.all(result_df.index.get_level_values("electronId") == 0)
+
+    # pulse ids should span 0-499 on each train
+    for train_id in result_df.index.get_level_values("trainId"):
+        assert np.all(
+            result_df.loc[train_id].index.get_level_values("pulseId").values == np.arange(500),
+        )
+    # assert index uniqueness
+    assert result_df.index.is_unique
+
+    # assert that dataframe contains all channels
+    assert np.all(
+        set(result_df.columns) == set(get_channels(config_dataframe, ["per_pulse"])),
+    )
+
+
+def test_create_dataframe_per_train(config_dataframe: dict, h5_paths: list[Path]) -> None:
+    """Test the creation of a pandas DataFrame for a channel of type [per train]."""
+    df = DataFrameCreator(config_dataframe, h5_paths[0])
+    result_df = df.df_train
+
+    channel = "delayStage"
+    key, data = df.get_dataset_array(channel, slice_=True)
+
+    # Check that the result_df is a DataFrame and has the correct shape
+    assert isinstance(result_df, DataFrame)
+
+    # check that all values are in the df for delayStage
+    assert np.all(result_df[channel].dropna() == data[()])
+
+    # check that dataframe contains all channels
+    assert np.all(
+        set(result_df.columns)
+        == set(get_channels(config_dataframe, ["per_train"], extend_aux=True)),
+    )
+
+    # Ensure DataFrame has rows equal to unique keys from "per_train" channels, considering
+    # different channels may have data for different trains. This checks the DataFrame's
+    # completeness and integrity, especially important when channels record at varying trains.
+    channels = get_channels(config_dataframe, ["per_train"])
+    all_keys = Index([])
+    for channel in channels:
+        # Append unique keys from each channel, considering only training data
+        all_keys = all_keys.append(df.get_dataset_array(channel, slice_=True)[0])
+    # Verify DataFrame's row count matches unique train IDs count across channels
+    assert result_df.shape[0] == len(all_keys.unique())
+
+    # check index levels
+    assert set(result_df.index.names) == {"trainId", "pulseId", "electronId"}
+
+    # all pulseIds and electronIds should be 0
+    assert np.all(result_df.index.get_level_values("pulseId") == 0)
+    assert np.all(result_df.index.get_level_values("electronId") == 0)
+
+    channel = "dldAux"
+    key, data = df.get_dataset_array(channel, slice_=True)
+
+    # Check if the subchannels are correctly sliced into the dataframe
+    # The values are stored in DLD which is a 2D array
+    # The subchannels are stored in the second dimension
+    # Only index amount of values are stored in the first dimension, the rest are NaNs
+    # hence the slicing
+    subchannels = config_dataframe["channels"]["dldAux"]["sub_channels"]
+    for subchannel, values in subchannels.items():
+        assert np.all(df.df_train[subchannel].dropna().values == data[: key.size, values["slice"]])
+
+    assert result_df.index.is_unique
+
+
+def test_group_name_not_in_h5(config_dataframe: dict, h5_paths: list[Path]) -> None:
+    """Test ValueError when the group_name for a channel does not exist in the H5 file."""
+    channel = "dldPosX"
+    config = config_dataframe
+    config["channels"][channel]["dataset_key"] = "foo"
+    df = DataFrameCreator(config, h5_paths[0])
+
+    with pytest.raises(KeyError):
+        df.df_electron
+
+
+def test_create_dataframe_per_file(config_dataframe: dict, h5_paths: list[Path]) -> None:
+    """Test the creation of pandas DataFrames for a given file."""
+    df = DataFrameCreator(config_dataframe, h5_paths[0])
+    result_df = df.df
+
+    # Check that the result_df is a DataFrame and has the correct shape
+    assert isinstance(result_df, DataFrame)
+    all_keys = df.df_train.index.append(df.df_electron.index).append(df.df_pulse.index)
+    all_keys = all_keys.unique()
+    assert result_df.shape[0] == len(all_keys.unique())
+
+
+def test_get_index_dataset_key_error(config_dataframe: dict, h5_paths: list[Path]) -> None:
+    """
+    Test that a ValueError is raised when the dataset_key is missing for a channel in the config.
+    """
+    config = config_dataframe
+    channel = "dldPosX"
+    df = DataFrameCreator(config, h5_paths[0])
+
+    del config["channels"][channel]["dataset_key"]
+    with pytest.raises(ValueError):
+        df.get_index_dataset_key(channel)
diff --git a/tests/loader/flash/test_flash_loader.py b/tests/loader/flash/test_flash_loader.py
index edff997e..de0bdf35 100644
--- a/tests/loader/flash/test_flash_loader.py
+++ b/tests/loader/flash/test_flash_loader.py
@@ -1,93 +1,22 @@
 """Tests for FlashLoader functionality"""
+from __future__ import annotations
+
 import os
-from importlib.util import find_spec
 from pathlib import Path
 from typing import Literal
 
 import pytest
 
-from sed.core.config import parse_config
+from .test_buffer_handler import create_parquet_dir
 from sed.loader.flash.loader import FlashLoader
 
-package_dir = os.path.dirname(find_spec("sed").origin)
-config_path = os.path.join(package_dir, "../tests/data/loader/flash/config.yaml")
-H5_PATH = "FLASH1_USER3_stream_2_run43878_file1_20230130T153807.1.h5"
-
-
-@pytest.fixture(name="config_file")
-def fixture_config_file() -> dict:
-    """Fixture providing a configuration file for FlashLoader tests.
-
-    Returns:
-        dict: The parsed configuration file.
-    """
-    return parse_config(config_path)
-
-
-def test_get_channels_by_format(config_file: dict) -> None:
-    """
-    Test function to verify the 'get_channels' method in FlashLoader class for
-    retrieving channels based on formats and index inclusion.
-    """
-    # Initialize the FlashLoader instance with the given config_file.
-    fl = FlashLoader(config_file)
-
-    # Define expected channels for each format.
-    electron_channels = ["dldPosX", "dldPosY", "dldTimeSteps"]
-    pulse_channels = [
-        "sampleBias",
-        "tofVoltage",
-        "extractorVoltage",
-        "extractorCurrent",
-        "cryoTemperature",
-        "sampleTemperature",
-        "dldTimeBinSize",
-        "gmdTunnel",
-    ]
-    train_channels = ["timeStamp", "delayStage"]
-    index_channels = ["trainId", "pulseId", "electronId"]
-
-    # Call get_channels method with different format options.
-
-    # Request channels for 'per_electron' format using a list.
-    format_electron = fl.get_channels(["per_electron"])
-
-    # Request channels for 'per_pulse' format using a string.
-    format_pulse = fl.get_channels("per_pulse")
-
-    # Request channels for 'per_train' format using a list.
-    format_train = fl.get_channels(["per_train"])
-
-    # Request channels for 'all' formats using a list.
-    format_all = fl.get_channels(["all"])
-
-    # Request index channels only.
-    format_index = fl.get_channels(index=True)
-
-    # Request 'per_electron' format and include index channels.
-    format_index_electron = fl.get_channels(["per_electron"], index=True)
-
-    # Request 'all' formats and include index channels.
-    format_all_index = fl.get_channels(["all"], index=True)
-
-    # Assert that the obtained channels match the expected channels.
-    assert set(electron_channels) == set(format_electron)
-    assert set(pulse_channels) == set(format_pulse)
-    assert set(train_channels) == set(format_train)
-    assert set(electron_channels + pulse_channels + train_channels) == set(format_all)
-    assert set(index_channels) == set(format_index)
-    assert set(index_channels + electron_channels) == set(format_index_electron)
-    assert set(index_channels + electron_channels + pulse_channels + train_channels) == set(
-        format_all_index,
-    )
-
 
 @pytest.mark.parametrize(
     "sub_dir",
     ["online-0/fl1user3/", "express-0/fl1user3/", "FL1USER3/"],
 )
-def test_initialize_paths(
-    config_file: dict,
+def test_initialize_dirs(
+    config: dict,
     fs,
     sub_dir: Literal["online-0/fl1user3/", "express-0/fl1user3/", "FL1USER3/"],
 ) -> None:
@@ -98,128 +27,208 @@ def test_initialize_paths(
     fs: A fixture for a fake file system.
     sub_dir (Literal["online-0/fl1user3/", "express-0/fl1user3/", "FL1USER3/"]): Sub-directory.
     """
-    config = config_file
-    del config["core"]["paths"]
-    config["core"]["beamtime_id"] = "12345678"
-    config["core"]["year"] = "2000"
+    config_ = config.copy()
+    del config_["core"]["paths"]
+    config_["core"]["beamtime_id"] = "12345678"
+    config_["core"]["year"] = "2000"
 
     # Find base path of beamline from config. Here, we use pg2
-    base_path = config["dataframe"]["beamtime_dir"]["pg2"]
+    base_path = config_["core"]["beamtime_dir"]["pg2"]
     expected_path = (
-        Path(base_path) / config["core"]["year"] / "data" / config["core"]["beamtime_id"]
+        Path(base_path) / config_["core"]["year"] / "data" / config_["core"]["beamtime_id"]
     )
     # Create expected paths
     expected_raw_path = expected_path / "raw" / "hdf" / sub_dir
-    expected_processed_path = expected_path / "processed" / "parquet"
+    expected_processed_path = expected_path / "processed"
 
     # Create a fake file system for testing
     fs.create_dir(expected_raw_path)
     fs.create_dir(expected_processed_path)
 
-    # Instance of class with correct config and call initialize_paths
-    fl = FlashLoader(config=config)
-    data_raw_dir, data_parquet_dir = fl.initialize_paths()
+    # Instance of class with correct config and call initialize_dirs
+    fl = FlashLoader(config=config_)
+    fl._initialize_dirs()
+    assert str(expected_raw_path) == fl.raw_dir
+    assert str(expected_processed_path) == fl.processed_dir
 
-    assert expected_raw_path == data_raw_dir[0]
-    assert expected_processed_path == data_parquet_dir
+    # remove beamtime_id, year and daq from config to raise error
+    del config_["core"]["beamtime_id"]
+    with pytest.raises(ValueError) as e:
+        fl._initialize_dirs()
+    assert "The beamtime_id and year are required." in str(e.value)
 
 
-def test_initialize_paths_filenotfound(config_file: dict) -> None:
+def test_initialize_dirs_filenotfound(config: dict) -> None:
     """
     Test FileNotFoundError during the initialization of paths.
     """
     # Test the FileNotFoundError
-    config = config_file
-    del config["core"]["paths"]
-    config["core"]["beamtime_id"] = "11111111"
-    config["core"]["year"] = "2000"
+    config_ = config.copy()
+    del config_["core"]["paths"]
+    config_["core"]["beamtime_id"] = "11111111"
+    config_["core"]["year"] = "2000"
 
-    # Instance of class with correct config and call initialize_paths
-    fl = FlashLoader(config=config)
+    # Instance of class with correct config and call initialize_dirs
     with pytest.raises(FileNotFoundError):
-        _, _ = fl.initialize_paths()
+        fl = FlashLoader(config=config_)
+        fl._initialize_dirs()
 
 
-def test_invalid_channel_format(config_file: dict) -> None:
+def test_save_read_parquet_flash(config: dict) -> None:
     """
-    Test ValueError for an invalid channel format.
+    Test the functionality of saving and reading parquet files with FlashLoader.
+
+    This test performs three main actions:
+    1. First call to create and read parquet files. Verifies new files are created.
+    2. Second call with the same parameters to check that it only reads from
+    the existing parquet files without creating new ones. It asserts that the files' modification
+    times remain unchanged, indicating no new files were created or existing files overwritten.
+    3. Third call with `force_recreate=True` to force the recreation of parquet files.
+    It verifies that the files were indeed overwritten by checking that their modification
+    times have changed.
     """
-    config = config_file
-    config["dataframe"]["channels"]["dldPosX"]["format"] = "foo"
+    config_ = config.copy()
+    data_parquet_dir = create_parquet_dir(config_, "flash_save_read")
+    config_["core"]["paths"]["processed"] = data_parquet_dir
+    fl = FlashLoader(config=config_)
+
+    # First call: should create and read the parquet file
+    df1, _, _ = fl.read_dataframe(runs=[43878, 43879])
+    # Check if new files were created
+    data_parquet_dir = data_parquet_dir.joinpath("buffer")
+    new_files = {
+        file: os.path.getmtime(data_parquet_dir.joinpath(file))
+        for file in os.listdir(data_parquet_dir)
+    }
+    assert new_files
 
-    fl = FlashLoader(config=config)
+    # Second call: should only read the parquet file, not create new ones
+    df2, _, _ = fl.read_dataframe(runs=[43878, 43879])
 
-    with pytest.raises(ValueError):
-        fl.read_dataframe()
+    # Verify no new files were created after the second call
+    final_files = {
+        file: os.path.getmtime(data_parquet_dir.joinpath(file))
+        for file in os.listdir(data_parquet_dir)
+    }
+    assert (
+        new_files == final_files
+    ), "Files were overwritten or new files were created after the second call."
 
+    # Third call: We force_recreate the parquet files
+    df3, _, _ = fl.read_dataframe(runs=[43878, 43879], force_recreate=True)
 
-def test_group_name_not_in_h5(config_file: dict) -> None:
-    """
-    Test ValueError when the group_name for a channel does not exist in the H5 file.
-    """
-    config = config_file
-    config["dataframe"]["channels"]["dldPosX"]["group_name"] = "foo"
-    fl = FlashLoader(config=config)
+    # Verify files were overwritten
+    new_files = {
+        file: os.path.getmtime(data_parquet_dir.joinpath(file))
+        for file in os.listdir(data_parquet_dir)
+    }
+    assert new_files != final_files, "Files were not overwritten after the third call."
 
-    with pytest.raises(ValueError) as e:
-        fl.create_dataframe_per_file(Path(config["core"]["paths"]["data_raw_dir"] + H5_PATH))
+    # remove the parquet files
+    for file in new_files:
+        data_parquet_dir.joinpath(file).unlink()
 
-    assert str(e.value.args[0]) == "The group_name for channel dldPosX does not exist."
 
+def test_get_elapsed_time_fid(config: dict) -> None:
+    """Test get_elapsed_time method of FlashLoader class"""
+    # Create an instance of FlashLoader
+    fl = FlashLoader(config=config)
 
-def test_buffer_schema_mismatch(config_file: dict) -> None:
-    """
-    Test function to verify schema mismatch handling in the FlashLoader's 'read_dataframe' method.
-
-    The test validates the error handling mechanism when the available channels do not match the
-    schema of the existing parquet files.
-
-    Test Steps:
-    - Attempt to read a dataframe after adding a new channel 'gmdTunnel2' to the configuration.
-    - Check for an expected error related to the mismatch between available channels and schema.
-    - Force recreation of dataframe with the added channel, ensuring successful dataframe creation.
-    - Simulate a missing channel scenario by removing 'gmdTunnel2' from the configuration.
-    - Check for an error indicating a missing channel in the configuration.
-    - Clean up created buffer files after the test.
-    """
-    fl = FlashLoader(config=config_file)
+    # Mock the file_statistics and files
+    fl.metadata = {
+        "file_statistics": {
+            "timed": {
+                "0": {"columns": {"timeStamp": {"min": 10, "max": 20}}},
+                "1": {"columns": {"timeStamp": {"min": 20, "max": 30}}},
+                "2": {"columns": {"timeStamp": {"min": 30, "max": 40}}},
+            },
+        },
+    }
+    fl.files = ["file0", "file1", "file2"]
+
+    # Test get_elapsed_time with fids
+    assert fl.get_elapsed_time(fids=[0, 1]) == 20
+
+    # # Test get_elapsed_time with runs
+    # # Assuming get_files_from_run_id(43878) returns ["file0", "file1"]
+    # assert fl.get_elapsed_time(runs=[43878]) == 20
+
+    # Test get_elapsed_time with aggregate=False
+    assert fl.get_elapsed_time(fids=[0, 1], aggregate=False) == [10, 10]
+
+    # Test KeyError when file_statistics is missing
+    fl.metadata = {"something": "else"}
+    with pytest.raises(KeyError) as e:
+        fl.get_elapsed_time(fids=[0, 1])
+
+    assert "File statistics missing. Use 'read_dataframe' first." in str(e.value)
+    # Test KeyError when time_stamps is missing
+    fl.metadata = {
+        "file_statistics": {
+            "timed": {
+                "0": {},
+                "1": {"columns": {"timeStamp": {"min": 20, "max": 30}}},
+            },
+        },
+    }
+    with pytest.raises(KeyError) as e:
+        fl.get_elapsed_time(fids=[0, 1])
+
+    assert "Timestamp metadata missing in file 0" in str(e.value)
 
-    # Read a dataframe for a specific run
-    fl.read_dataframe(runs=["43878"])
 
-    # Manipulate the configuration to introduce a new channel 'gmdTunnel2'
-    config = config_file
-    config["dataframe"]["channels"]["gmdTunnel2"] = {
-        "group_name": "/FL1/Photon Diagnostic/GMD/Pulse resolved energy/energy tunnel/",
-        "format": "per_pulse",
+def test_get_elapsed_time_run(config: dict) -> None:
+    """Test get_elapsed_time method of FlashLoader class"""
+    config_ = config.copy()
+    config_["core"]["paths"] = {
+        "raw": "tests/data/loader/flash/",
+        "processed": "tests/data/loader/flash/parquet/get_elapsed_time_run",
     }
+    config_ = config.copy()
+    data_parquet_dir = create_parquet_dir(config_, "get_elapsed_time_run")
+    config_["core"]["paths"]["processed"] = data_parquet_dir
+    # Create an instance of FlashLoader
+    fl = FlashLoader(config=config_)
 
-    # Reread the dataframe with the modified configuration, expecting a schema mismatch error
-    fl = FlashLoader(config=config)
-    with pytest.raises(ValueError) as e:
-        fl.read_dataframe(runs=["43878"])
-    expected_error = e.value.args
+    fl.read_dataframe(runs=[43878, 43879])
+    min_max = fl.metadata["file_statistics"]["electron"]["0"]["columns"]["timeStamp"]
+    expected_elapsed_time_0 = min_max["max"] - min_max["min"]
+    min_max = fl.metadata["file_statistics"]["electron"]["1"]["columns"]["timeStamp"]
+    expected_elapsed_time_1 = min_max["max"] - min_max["min"]
+
+    elapsed_time = fl.get_elapsed_time(runs=[43878])
+    assert elapsed_time == expected_elapsed_time_0
+
+    elapsed_time = fl.get_elapsed_time(runs=[43878, 43879], aggregate=False)
+    assert elapsed_time == [expected_elapsed_time_0, expected_elapsed_time_1]
 
-    # Validate the specific error messages for schema mismatch
-    assert "The available channels do not match the schema of file" in expected_error[0]
-    assert expected_error[2] == "Missing in parquet: {'gmdTunnel2'}"
-    assert expected_error[4] == "Please check the configuration file or set force_recreate to True."
+    elapsed_time = fl.get_elapsed_time(runs=[43878, 43879])
+    assert elapsed_time == expected_elapsed_time_0 + expected_elapsed_time_1
 
-    # Force recreation of the dataframe, including the added channel 'gmdTunnel2'
-    fl.read_dataframe(runs=["43878"], force_recreate=True)
+    # remove the parquet files
+    for file in os.listdir(Path(fl.processed_dir, "buffer")):
+        Path(fl.processed_dir, "buffer").joinpath(file).unlink()
 
-    # Remove 'gmdTunnel2' from the configuration to simulate a missing channel scenario
-    del config["dataframe"]["channels"]["gmdTunnel2"]
+
+def test_available_runs(monkeypatch: pytest.MonkeyPatch, config: dict) -> None:
+    """Test available_runs property of FlashLoader class"""
+    # Create an instance of FlashLoader
     fl = FlashLoader(config=config)
-    with pytest.raises(ValueError) as e:
-        # Attempt to read the dataframe again to check for the missing channel error
-        fl.read_dataframe(runs=["43878"])
 
-    expected_error = e.value.args
-    # Check for the specific error message indicating a missing channel in the configuration
-    assert expected_error[3] == "Missing in config: {'gmdTunnel2'}"
+    # Mock the raw_dir and files
+    fl.raw_dir = "/path/to/raw_dir"
+    files = [
+        "run1_file1.h5",
+        "run3_file1.h5",
+        "run2_file1.h5",
+        "run1_file2.h5",
+    ]
+
+    # Mock the glob method to return the mock files
+    def mock_glob(*args, **kwargs):  # noqa: ARG001
+        return [Path(fl.raw_dir, file) for file in files]
+
+    monkeypatch.setattr(Path, "glob", mock_glob)
 
-    # Clean up created buffer files after the test
-    _, parquet_data_dir = fl.initialize_paths()
-    for file in os.listdir(Path(parquet_data_dir, "buffer")):
-        os.remove(Path(parquet_data_dir, "buffer", file))
+    # Test available_runs
+    assert fl.available_runs == [1, 2, 3]
diff --git a/tests/loader/flash/test_flash_metadata.py b/tests/loader/flash/test_flash_metadata.py
index ef5305ad..165a6e8a 100644
--- a/tests/loader/flash/test_flash_metadata.py
+++ b/tests/loader/flash/test_flash_metadata.py
@@ -1,59 +1,96 @@
+"""Tests for FlashLoader metadata functionality"""
+from __future__ import annotations
+
+import os
+
 import pytest
 
+from sed.core.config import read_env_var
+from sed.core.config import save_env_var
+from sed.core.config import USER_CONFIG_PATH
 from sed.loader.flash.metadata import MetadataRetriever
 
+ENV_PATH = USER_CONFIG_PATH / ".env"
+
 
 @pytest.fixture
-def mock_requests(requests_mock):
+def mock_requests(requests_mock) -> None:
     # Mocking the response for the dataset URL
     dataset_url = "https://example.com/Datasets/11013410%2F43878"
     requests_mock.get(dataset_url, json={"fake": "data"}, status_code=200)
 
 
-# Test cases for MetadataRetriever
-def test_get_metadata(mock_requests):  # noqa: ARG001
+def test_get_metadata_with_explicit_token(mock_requests: None) -> None:  # noqa: ARG001
     metadata_config = {
-        "scicat_url": "https://example.com",
-        "scicat_token": "fake_token",
+        "archiver_url": "https://example.com",
     }
-    retriever = MetadataRetriever(metadata_config)
+    retriever = MetadataRetriever(metadata_config, token="explicit_test_token")
     metadata = retriever.get_metadata("11013410", ["43878"])
     assert isinstance(metadata, dict)
     assert metadata == {"fake": "data"}
+    assert ENV_PATH.exists()
+    assert read_env_var("SCICAT_TOKEN") == "explicit_test_token"
+    os.remove(ENV_PATH)
 
 
-def test_get_metadata_with_existing_metadata(mock_requests):  # noqa: ARG001
+def test_get_metadata_with_env_token(mock_requests: None) -> None:  # noqa: ARG001
+    save_env_var("SCICAT_TOKEN", "env_test_token")
     metadata_config = {
-        "scicat_url": "https://example.com",
-        "scicat_token": "fake_token",
+        "archiver_url": "https://example.com",
     }
     retriever = MetadataRetriever(metadata_config)
+    metadata = retriever.get_metadata("11013410", ["43878"])
+    assert isinstance(metadata, dict)
+    assert metadata == {"fake": "data"}
+    os.remove(ENV_PATH)
+
+
+def test_get_metadata_no_token() -> None:
+    metadata_config = {
+        "archiver_url": "https://example.com",
+    }
+    with pytest.raises(ValueError, match="Token is required for metadata collection"):
+        MetadataRetriever(metadata_config)
+
+
+def test_get_metadata_no_url() -> None:
+    metadata_config: dict = {}
+    with pytest.raises(ValueError, match="No URL provided for fetching metadata"):
+        MetadataRetriever(metadata_config, token="test_token")
+    os.remove(ENV_PATH)
+
+
+def test_get_metadata_with_existing_metadata(mock_requests: None) -> None:  # noqa: ARG001
+    metadata_config = {
+        "archiver_url": "https://example.com",
+    }
+    retriever = MetadataRetriever(metadata_config, token="test_token")
     existing_metadata = {"existing": "metadata"}
     metadata = retriever.get_metadata("11013410", ["43878"], existing_metadata)
     assert isinstance(metadata, dict)
     assert metadata == {"existing": "metadata", "fake": "data"}
+    os.remove(ENV_PATH)
 
 
-def test_get_metadata_per_run(mock_requests):  # noqa: ARG001
+def test_get_metadata_per_run(mock_requests: None) -> None:  # noqa: ARG001
     metadata_config = {
-        "scicat_url": "https://example.com",
-        "scicat_token": "fake_token",
+        "archiver_url": "https://example.com",
     }
-    retriever = MetadataRetriever(metadata_config)
+    retriever = MetadataRetriever(metadata_config, token="test_token")
     metadata = retriever._get_metadata_per_run("11013410/43878")
     assert isinstance(metadata, dict)
     assert metadata == {"fake": "data"}
+    os.remove(ENV_PATH)
 
 
-def test_create_dataset_url_by_PID():
+def test_create_dataset_url_by_PID() -> None:
     metadata_config = {
-        "scicat_url": "https://example.com",
-        "scicat_token": "fake_token",
+        "archiver_url": "https://example.com",
     }
-    retriever = MetadataRetriever(metadata_config)
-    # Assuming the dataset follows the new format
+    retriever = MetadataRetriever(metadata_config, token="test_token")
     pid = "11013410/43878"
     url = retriever._create_new_dataset_url(pid)
     expected_url = "https://example.com/Datasets/11013410%2F43878"
     assert isinstance(url, str)
     assert url == expected_url
+    os.remove(ENV_PATH)
diff --git a/tests/loader/flash/test_utils.py b/tests/loader/flash/test_utils.py
new file mode 100644
index 00000000..929a9305
--- /dev/null
+++ b/tests/loader/flash/test_utils.py
@@ -0,0 +1,75 @@
+"""Tests for utils functionality"""
+from sed.loader.flash.utils import get_channels
+
+# Define expected channels for each format.
+ELECTRON_CHANNELS = ["dldPosX", "dldPosY", "dldTimeSteps"]
+PULSE_CHANNELS = ["pulserSignAdc", "gmdTunnel"]
+TRAIN_CHANNELS = ["timeStamp", "delayStage", "dldAux"]
+TRAIN_CHANNELS_EXTENDED = [
+    "sampleBias",
+    "tofVoltage",
+    "extractorVoltage",
+    "extractorCurrent",
+    "cryoTemperature",
+    "sampleTemperature",
+    "dldTimeBinSize",
+    "timeStamp",
+    "delayStage",
+]
+INDEX_CHANNELS = ["trainId", "pulseId", "electronId"]
+
+
+def test_get_channels_by_format(config_dataframe: dict) -> None:
+    """
+    Test function to verify the 'get_channels' method in FlashLoader class for
+    retrieving channels based on formats and index inclusion.
+    """
+    # Initialize the FlashLoader instance with the given config_file.
+    ch_dict = config_dataframe
+
+    # Call get_channels method with different format options.
+
+    # Request channels for 'per_electron' format using a list.
+    print(ch_dict["channels"])
+    format_electron = get_channels(ch_dict, ["per_electron"])
+
+    # Request channels for 'per_pulse' format using a string.
+    format_pulse = get_channels(ch_dict, "per_pulse")
+
+    # Request channels for 'per_train' format without expanding the dldAuxChannels.
+    format_train = get_channels(ch_dict, "per_train", extend_aux=False)
+
+    # Request channels for 'per_train' format using a list, and expand the dldAuxChannels.
+    format_train_extended = get_channels(ch_dict, ["per_train"], extend_aux=True)
+
+    # Request channels for 'all' formats using a list.
+    format_all = get_channels(ch_dict, ["all"])
+
+    # Request index channels only. No need for channel_dict.
+    format_index = get_channels(index=True)
+
+    # Request 'per_electron' format and include index channels.
+    format_index_electron = get_channels(ch_dict, ["per_electron"], index=True)
+
+    # Request 'all' formats and include index channels.
+    format_all_index = get_channels(ch_dict, ["all"], index=True)
+
+    # Request 'all' formats and include index channels and extend aux channels
+    format_all_index_extend_aux = get_channels(ch_dict, ["all"], index=True, extend_aux=True)
+
+    # Assert that the obtained channels match the expected channels.
+    assert set(ELECTRON_CHANNELS) == set(format_electron)
+    assert set(TRAIN_CHANNELS_EXTENDED) == set(format_train_extended)
+    assert set(TRAIN_CHANNELS) == set(format_train)
+    assert set(PULSE_CHANNELS) == set(format_pulse)
+    assert set(ELECTRON_CHANNELS + TRAIN_CHANNELS + PULSE_CHANNELS) == set(format_all)
+    assert set(INDEX_CHANNELS) == set(format_index)
+    assert set(INDEX_CHANNELS + ELECTRON_CHANNELS) == set(format_index_electron)
+    assert set(INDEX_CHANNELS + ELECTRON_CHANNELS + TRAIN_CHANNELS + PULSE_CHANNELS) == set(
+        format_all_index,
+    )
+    assert set(
+        INDEX_CHANNELS + ELECTRON_CHANNELS + PULSE_CHANNELS + TRAIN_CHANNELS_EXTENDED,
+    ) == set(
+        format_all_index_extend_aux,
+    )
diff --git a/tests/loader/mpes/__init__.py b/tests/loader/mpes/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/tests/loader/mpes/test_mpes_loader.py b/tests/loader/mpes/test_mpes_loader.py
new file mode 100644
index 00000000..d48a58d2
--- /dev/null
+++ b/tests/loader/mpes/test_mpes_loader.py
@@ -0,0 +1,80 @@
+"""Tests specific for Mpes loader"""
+from __future__ import annotations
+
+import logging
+import os
+from copy import deepcopy
+
+import pytest
+
+from sed.core.config import parse_config
+from sed.loader.mpes.loader import MpesLoader
+
+test_dir = os.path.join(os.path.dirname(__file__), "../..")
+test_data_dir = os.path.join(test_dir, "data/loader/mpes")
+
+config = parse_config(
+    os.path.join(test_data_dir, "config.yaml"),
+    folder_config={},
+    user_config={},
+    system_config={},
+)
+
+
+def test_channel_not_found_warning(caplog) -> None:
+    """Test if the mpes loader gives the correct warning if a channel cannot be found."""
+    ml = MpesLoader(config=config)
+
+    with caplog.at_level(logging.WARNING):
+        ml.read_dataframe(folders=test_data_dir)
+    assert not caplog.messages
+
+    # modify per_file channel
+    config_ = deepcopy(config)
+    config_["dataframe"]["channels"]["sampleBias"]["dataset_key"] = "invalid"
+    ml = MpesLoader(config=config_)
+
+    caplog.clear()
+    with caplog.at_level(logging.WARNING):
+        ml.read_dataframe(folders=test_data_dir)
+    assert 'Entry "invalid" for channel "sampleBias" not found.' in caplog.messages[0]
+
+    # modify per_electron channel
+    config_ = deepcopy(config)
+    config_["dataframe"]["channels"]["X"]["dataset_key"] = "invalid"
+    ml = MpesLoader(config=config_)
+
+    caplog.clear()
+    with caplog.at_level(logging.WARNING):
+        ml.read_dataframe(folders=test_data_dir)
+    assert 'Entry "invalid" for channel "X" not found.' in caplog.messages[0]
+
+
+def test_invalid_channel_format_raises() -> None:
+    """Test if the mpes loader raises an exception if an illegal channel format is provided."""
+    config_ = deepcopy(config)
+    config_["dataframe"]["channels"]["sampleBias"]["format"] = "per_train"
+    ml = MpesLoader(config=config_)
+
+    with pytest.raises(ValueError) as e:
+        ml.read_dataframe(folders=test_data_dir)
+
+    expected_error = e.value.args[0]
+
+    assert "Invalid 'format':per_train for channel sampleBias." in expected_error
+
+
+def test_no_electron_channels_raises() -> None:
+    """Test if the mpes loader raises an exception if no per-electron channels are provided."""
+    config_ = deepcopy(config)
+    config_["dataframe"]["channels"] = {
+        "sampleBias": {"format": "per_file", "dataset_key": "KTOF:Lens:Sample:V"},
+    }
+    ml = MpesLoader(config=config_)
+
+    with pytest.raises(ValueError) as e:
+        ml.read_dataframe(folders=test_data_dir)
+
+    expected_error = e.value.args[0]
+
+    assert "No valid 'per_electron' channels found." in expected_error
diff --git a/tests/loader/sxp/test_sxp_loader.py b/tests/loader/sxp/test_sxp_loader.py
index 3332f231..669003c8 100644
--- a/tests/loader/sxp/test_sxp_loader.py
+++ b/tests/loader/sxp/test_sxp_loader.py
@@ -1,17 +1,16 @@
-# pylint: disable=duplicate-code
 """Tests for SXPLoader functionality"""
+from __future__ import annotations
+
 import os
-from importlib.util import find_spec
 from pathlib import Path
-from typing import List
 
 import pytest
 
 from sed.core.config import parse_config
 from sed.loader.sxp.loader import SXPLoader
 
-package_dir = os.path.dirname(find_spec("sed").origin)
-config_path = os.path.join(package_dir, "../tests/data/loader/sxp/config.yaml")
+test_dir = os.path.join(os.path.dirname(__file__), "../..")
+config_path = os.path.join(test_dir, "data/loader/sxp/config.yaml")
 H5_PATH = "RAW-R0016-DA03-S00000.h5"
 
 
@@ -35,7 +34,7 @@ def test_get_channels_by_format(config_file: dict) -> None:
 
     # Define expected channels for each format.
     electron_channels = ["dldPosX", "dldPosY", "dldTimeSteps"]
-    pulse_channels: List[str] = []
+    pulse_channels: list[str] = []
     train_channels = ["timeStamp", "delayStage"]
     index_channels = ["trainId", "pulseId", "electronId"]
 
@@ -74,7 +73,7 @@ def test_get_channels_by_format(config_file: dict) -> None:
     )
 
 
-def test_initialize_paths(config_file: dict, fs) -> None:
+def test_initialize_dirs(config_file: dict, fs) -> None:
     """
     Test the initialization of paths based on the configuration and directory structures.
 
@@ -87,7 +86,7 @@ def test_initialize_paths(config_file: dict, fs) -> None:
     config["core"]["year"] = "2000"
 
     # Find base path of beamline from config.
-    base_path = config["dataframe"]["beamtime_dir"]["sxp"]
+    base_path = config["core"]["beamtime_dir"]["sxp"]
     expected_path = Path(base_path) / config["core"]["year"] / config["core"]["beamtime_id"]
     # Create expected paths
     expected_raw_path = expected_path / "raw"
@@ -97,15 +96,15 @@ def test_initialize_paths(config_file: dict, fs) -> None:
     fs.create_dir(expected_raw_path)
     fs.create_dir(expected_processed_path)
 
-    # Instance of class with correct config and call initialize_paths
+    # Instance of class with correct config and call initialize_dirs
     sl = SXPLoader(config=config)
-    data_raw_dir, data_parquet_dir = sl.initialize_paths()
+    sl._initialize_dirs()
 
-    assert expected_raw_path == data_raw_dir[0]
-    assert expected_processed_path == data_parquet_dir
+    assert expected_raw_path == sl.raw_dir[0]
+    assert expected_processed_path == sl.processed_dir
 
 
-def test_initialize_paths_filenotfound(config_file: dict):
+def test_initialize_dirs_filenotfound(config_file: dict):
     """
     Test FileNotFoundError during the initialization of paths.
     """
@@ -115,10 +114,10 @@ def test_initialize_paths_filenotfound(config_file: dict):
     config["core"]["beamtime_id"] = "11111111"
     config["core"]["year"] = "2000"
 
-    # Instance of class with correct config and call initialize_paths
+    # Instance of class with correct config and call initialize_dirs
     sl = SXPLoader(config=config)
     with pytest.raises(FileNotFoundError):
-        _, _ = sl.initialize_paths()
+        sl._initialize_dirs()
 
 
 def test_invalid_channel_format(config_file: dict):
@@ -150,7 +149,7 @@ def test_data_keys_not_in_h5(config_file: dict, key_type: str):
     sl = SXPLoader(config=config)
 
     with pytest.raises(ValueError) as e:
-        sl.create_dataframe_per_file(config["core"]["paths"]["data_raw_dir"] + H5_PATH)
+        sl.create_dataframe_per_file(Path(config["core"]["paths"]["raw"], H5_PATH))
 
     assert str(e.value.args[0]) == f"The {key_type} for channel dldPosX does not exist."
 
@@ -209,6 +208,6 @@ def test_buffer_schema_mismatch(config_file: dict):
     assert expected_error[3] == "Missing in config: {'delayStage2'}"
 
     # Clean up created buffer files after the test
-    _, parquet_data_dir = sl.initialize_paths()
-    for file in os.listdir(Path(parquet_data_dir, "buffer")):
-        os.remove(Path(parquet_data_dir, "buffer", file))
+    sl._initialize_dirs()
+    for file in os.listdir(Path(sl.processed_dir, "buffer")):
+        os.remove(Path(sl.processed_dir, "buffer", file))
diff --git a/tests/loader/test_loaders.py b/tests/loader/test_loaders.py
index f638ba0d..a5b357d0 100644
--- a/tests/loader/test_loaders.py
+++ b/tests/loader/test_loaders.py
@@ -1,11 +1,11 @@
 """Test cases for loaders used to load dataframes
 """
+from __future__ import annotations
+
 import os
 from copy import deepcopy
-from importlib.util import find_spec
 from pathlib import Path
 from typing import cast
-from typing import List
 
 import dask.dataframe as ddf
 import pytest
@@ -18,9 +18,8 @@
 from sed.loader.loader_interface import get_names_of_all_loaders
 from sed.loader.utils import gather_files
 
-package_dir = os.path.dirname(find_spec("sed").origin)
-
-test_data_dir = os.path.join(package_dir, "..", "tests", "data")
+test_dir = os.path.join(os.path.dirname(__file__), "..")
+test_data_dir = os.path.join(test_dir, "data")
 
 read_types = ["one_file", "files", "one_folder", "folders", "one_run", "runs"]
 runs = {"generic": None, "mpes": ["30", "50"], "flash": ["43878", "43878"], "sxp": ["0016", "0016"]}
@@ -45,6 +44,9 @@ def get_loader_name_from_loader_object(loader: BaseLoader) -> str:
                     loader_name,
                     "config.yaml",
                 ),
+                folder_config={},
+                user_config={},
+                system_config={},
             ),
         )
         if loader.__name__ is gotten_loader.__name__:
@@ -52,7 +54,7 @@ def get_loader_name_from_loader_object(loader: BaseLoader) -> str:
     return ""
 
 
-def get_all_loaders() -> List[ParameterSet]:
+def get_all_loaders() -> list[ParameterSet]:
     """Scans through the loader list and returns them for pytest parametrization"""
     loaders = []
 
@@ -66,6 +68,9 @@ def get_all_loaders() -> List[ParameterSet]:
                     loader_name,
                     "config.yaml",
                 ),
+                folder_config={},
+                user_config={},
+                system_config={},
             ),
         )
         for loader_name in get_names_of_all_loaders()
@@ -91,8 +96,9 @@ def test_has_correct_read_dataframe_func(loader: BaseLoader, read_type: str) ->
     # Fix for race condition during parallel testing
     if loader.__name__ in {"flash", "sxp"}:
         config = deepcopy(loader._config)  # pylint: disable=protected-access
-        config["core"]["paths"]["data_parquet_dir"] = (
-            config["core"]["paths"]["data_parquet_dir"] + f"_{read_type}"
+        config["core"]["paths"]["processed"] = Path(
+            config["core"]["paths"]["processed"],
+            f"_{read_type}",
         )
         loader = get_loader(loader_name=loader.__name__, config=config)
 
@@ -163,9 +169,9 @@ def test_has_correct_read_dataframe_func(loader: BaseLoader, read_type: str) ->
 
     if loader.__name__ in {"flash", "sxp"}:
         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))
+        loader._initialize_dirs()
+        for file in os.listdir(Path(loader.processed_dir, "buffer")):
+            os.remove(Path(loader.processed_dir, "buffer", file))
 
 
 @pytest.mark.parametrize("loader", get_all_loaders())
@@ -179,8 +185,9 @@ def test_timed_dataframe(loader: BaseLoader) -> None:
     # Fix for race condition during parallel testing
     if loader.__name__ in {"flash", "sxp"}:
         config = deepcopy(loader._config)  # pylint: disable=protected-access
-        config["core"]["paths"]["data_parquet_dir"] = (
-            config["core"]["paths"]["data_parquet_dir"] + "_timed_dataframe"
+        config["core"]["paths"]["processed"] = Path(
+            config["core"]["paths"]["processed"],
+            "_timed_dataframe",
         )
         loader = get_loader(loader_name=loader.__name__, config=config)
 
@@ -196,9 +203,9 @@ def test_timed_dataframe(loader: BaseLoader) -> None:
             if loaded_timed_dataframe is None:
                 if loader.__name__ in {"flash", "sxp"}:
                     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))
+                    loader._initialize_dirs()
+                    for file in os.listdir(Path(loader.processed_dir, "buffer")):
+                        os.remove(Path(loader.processed_dir, "buffer", file))
                 pytest.skip("Not implemented")
             assert isinstance(loaded_timed_dataframe, ddf.DataFrame)
             assert set(loaded_timed_dataframe.columns).issubset(set(loaded_dataframe.columns))
@@ -206,9 +213,9 @@ def test_timed_dataframe(loader: BaseLoader) -> None:
 
     if loader.__name__ in {"flash", "sxp"}:
         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))
+        loader._initialize_dirs()
+        for file in os.listdir(Path(loader.processed_dir, "buffer")):
+            os.remove(Path(loader.processed_dir, "buffer", file))
 
 
 @pytest.mark.parametrize("loader", get_all_loaders())
@@ -222,8 +229,9 @@ def test_get_count_rate(loader: BaseLoader) -> None:
     # Fix for race condition during parallel testing
     if loader.__name__ in {"flash", "sxp"}:
         config = deepcopy(loader._config)  # pylint: disable=protected-access
-        config["core"]["paths"]["data_parquet_dir"] = (
-            config["core"]["paths"]["data_parquet_dir"] + "_count_rate"
+        config["core"]["paths"]["processed"] = Path(
+            config["core"]["paths"]["processed"],
+            "_count_rate",
         )
         loader = get_loader(loader_name=loader.__name__, config=config)
 
@@ -240,20 +248,24 @@ def test_get_count_rate(loader: BaseLoader) -> None:
             if loaded_time is None and loaded_countrate is None:
                 if loader.__name__ in {"flash", "sxp"}:
                     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))
+                    loader._initialize_dirs()
+                    for file in os.listdir(Path(loader.processed_dir, "buffer")):
+                        os.remove(Path(loader.processed_dir, "buffer", file))
                 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)
 
+            # illegal keywords
+            with pytest.raises(TypeError):
+                loader.get_count_rate(illegal_kwd=True)
+
     if loader.__name__ in {"flash", "sxp"}:
         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))
+        loader._initialize_dirs()
+        for file in os.listdir(Path(loader.processed_dir, "buffer")):
+            os.remove(Path(loader.processed_dir, "buffer", file))
 
 
 @pytest.mark.parametrize("loader", get_all_loaders())
@@ -267,8 +279,9 @@ def test_get_elapsed_time(loader: BaseLoader) -> None:
     # Fix for race condition during parallel testing
     if loader.__name__ in {"flash", "sxp"}:
         config = deepcopy(loader._config)  # pylint: disable=protected-access
-        config["core"]["paths"]["data_parquet_dir"] = (
-            config["core"]["paths"]["data_parquet_dir"] + "_elapsed_time"
+        config["core"]["paths"]["processed"] = Path(
+            config["core"]["paths"]["processed"],
+            "_elapsed_time",
         )
         loader = get_loader(loader_name=loader.__name__, config=config)
 
@@ -283,22 +296,26 @@ def test_get_elapsed_time(loader: BaseLoader) -> None:
             )
             elapsed_time = loader.get_elapsed_time()
             if elapsed_time is None:
-                if loader.__name__ in {"flash", "sxp"}:
+                if loader.__name__ in {"sxp"}:
                     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))
+                    loader._initialize_dirs()
+                    for file in os.listdir(Path(loader.processed_dir, "buffer")):
+                        os.remove(Path(loader.processed_dir, "buffer", file))
                 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
 
+            # illegal keywords
+            with pytest.raises(TypeError):
+                loader.get_elapsed_time(illegal_kwd=True)
+
     if loader.__name__ in {"flash", "sxp"}:
         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))
+        loader._initialize_dirs()
+        for file in os.listdir(Path(loader.processed_dir, "buffer")):
+            os.remove(Path(loader.processed_dir, "buffer", file))
 
 
 def test_mpes_timestamps() -> None:
diff --git a/tests/loader/test_mirrorutil.py b/tests/loader/test_mirrorutil.py
index 388345b1..e88b51ad 100644
--- a/tests/loader/test_mirrorutil.py
+++ b/tests/loader/test_mirrorutil.py
@@ -1,22 +1,23 @@
 """Module tests.loader.mirrorutil, tests for the sed.load.mirrorutil file
 """
+from __future__ import annotations
+
 import glob
 import io
 import os
 import shutil
 import tempfile
 from contextlib import redirect_stdout
-from importlib.util import find_spec
 
 import pytest
 
 from sed.loader.mirrorutil import CopyTool
 
 
-package_dir = os.path.dirname(find_spec("sed").origin)
-source_folder = package_dir + "/../"
-folder = package_dir + "/../tests/data/loader/mpes"
-file = package_dir + "/../tests/data/loader/mpes/Scan0030_2.h5"
+test_dir = os.path.join(os.path.dirname(__file__), "..")
+source_folder = test_dir + "/../"
+folder = test_dir + "/data/loader/mpes"
+file = test_dir + "/data/loader/mpes/Scan0030_2.h5"
 
 
 def test_copy_tool_folder() -> None:
diff --git a/tests/loader/test_utils.py b/tests/loader/test_utils.py
index 04a10748..6f342c7f 100644
--- a/tests/loader/test_utils.py
+++ b/tests/loader/test_utils.py
@@ -1,5 +1,7 @@
 """Module tests.loader.test_utils, tests for the sed.load.utils file
 """
+from __future__ import annotations
+
 import dask.dataframe as dd
 import numpy as np
 import pandas as pd
diff --git a/tests/test_binning.py b/tests/test_binning.py
index 417472f3..74cbd584 100644
--- a/tests/test_binning.py
+++ b/tests/test_binning.py
@@ -1,11 +1,10 @@
 """This file contains code that performs several tests for the sed.binning module
 """
+from __future__ import annotations
+
+from collections.abc import Sequence
 from typing import Any
 from typing import cast
-from typing import List
-from typing import Sequence
-from typing import Tuple
-from typing import Union
 
 import dask.dataframe as ddf
 import numpy as np
@@ -64,12 +63,12 @@
     [bins[:1], bins[:2], bins[:3]],
     ids=lambda x: f"bins:{len(x)}",
 )
-def test_histdd_error_is_raised(_samples: np.ndarray, _bins: List[int]) -> None:
+def test_histdd_error_is_raised(_samples: np.ndarray, _bins: list[int]) -> None:
     """Test if the correct error is raised if the bins and sample shapes do not match
 
     Args:
         _samples (np.ndarray): Samples array
-        _bins (List[Tuple]): Bins list
+        _bins (list[int]): Bins list
     """
     with pytest.raises(ValueError):
         if _samples.shape[1] == len(_bins):
@@ -95,12 +94,12 @@ def test_histdd_error_is_raised(_samples: np.ndarray, _bins: List[int]) -> None:
     if x[2] < 7
     else f"ndim: {x[2]-6}-round",
 )
-def test_histdd_bins_as_numpy(args: Tuple[np.ndarray, np.ndarray, int]) -> None:
+def test_histdd_bins_as_numpy(args: tuple[np.ndarray, np.ndarray, int]) -> None:
     """Test whether the numba_histogramdd functions produces the same result
     as np.histogramdd if called with a list of bin edges
 
     Args:
-        args (Tuple[np.ndarray, np.ndarray, int]): Tuple of
+        args (tuple[np.ndarray, np.ndarray, int]): Tuple of
             (samples, bin_edges, dimension)
     """
     sample_, bins_, _ = args
@@ -128,12 +127,12 @@ def test_histdd_bins_as_numpy(args: Tuple[np.ndarray, np.ndarray, int]) -> None:
     if x[3] < 7
     else f"ndim: {x[3]-6}-round",
 )
-def test_histdd_ranges_as_numpy(args: Tuple[np.ndarray, tuple, tuple, int]) -> None:
+def test_histdd_ranges_as_numpy(args: tuple[np.ndarray, tuple, tuple, int]) -> None:
     """Test whether the numba_histogramdd functions produces the same result
     as np.histogramdd if called with bin numbers and ranges
 
     Args:
-        args (Tuple[np.ndarray, np.ndarray, np.ndarray, int]): Tuple of
+        args (tuple[np.ndarray, np.ndarray, np.ndarray, int]): Tuple of
             (samples, bins, ranges, dimension)
     """
     sample_, bins_, ranges_, _ = args
@@ -161,12 +160,12 @@ def test_histdd_ranges_as_numpy(args: Tuple[np.ndarray, tuple, tuple, int]) -> N
     if x[3] < 7
     else f"ndim: {x[3]-6}-round",
 )
-def test_histdd_one_bins_as_numpy(args: Tuple[np.ndarray, int, tuple, int]) -> None:
+def test_histdd_one_bins_as_numpy(args: tuple[np.ndarray, int, tuple, int]) -> None:
     """Test whether the numba_histogramdd functions produces the same result
     as np.histogramdd if called with bin numbers and ranges
 
     Args:
-        args (Tuple[np.ndarray, np.ndarray, np.ndarray, int]): Tuple of
+        args (tuple[np.ndarray, np.ndarray, np.ndarray, int]): Tuple of
             (samples, bins, ranges, dimension)
     """
     sample_, bins_, ranges_, _ = args
@@ -195,13 +194,13 @@ def test_histdd_one_bins_as_numpy(args: Tuple[np.ndarray, int, tuple, int]) -> N
     else f"ndim: {x[4]-6}-round",
 )
 def test_from_bins_equals_from_bin_range(
-    args: Tuple[np.ndarray, int, tuple, np.ndarray, int],
+    args: tuple[np.ndarray, int, tuple, np.ndarray, int],
 ) -> None:
     """Test whether the numba_histogramdd functions produces the same result
     if called with bin numbers and ranges or with bin edges.
 
     Args:
-        args (Tuple[np.ndarray, int, tuple, np.ndarray, int]): Tuple of
+        args (tuple[np.ndarray, int, tuple, np.ndarray, int]): Tuple of
             (samples, bins, ranges, bin_edges, dimension)
     """
     sample_, bins_, ranges_, arrays_, _ = args
@@ -219,11 +218,11 @@ def test_from_bins_equals_from_bin_range(
     ],
     ids=lambda x: f"ndim: {x[2]}",
 )
-def test_numba_hist_from_bins(args: Tuple[np.ndarray, np.ndarray, int]) -> None:
+def test_numba_hist_from_bins(args: tuple[np.ndarray, np.ndarray, int]) -> None:
     """Run tests using the _hist_from_bins function without numba jit.
 
     Args:
-        args (Tuple[np.ndarray, np.ndarray, int]): Tuple of
+        args (tuple[np.ndarray, np.ndarray, int]): Tuple of
             (samples, bin_edges, dimension)
     """
     sample_, arrays_, _ = args
@@ -253,11 +252,11 @@ def test_numba_hist_from_bins(args: Tuple[np.ndarray, np.ndarray, int]) -> None:
     ],
     ids=lambda x: f"ndim: {x[3]}",
 )
-def test_numba_hist_from_bins_ranges(args: Tuple[np.ndarray, int, tuple, int]) -> None:
+def test_numba_hist_from_bins_ranges(args: tuple[np.ndarray, int, tuple, int]) -> None:
     """Run tests using the _hist_from_bins_ranges function without numba jit.
 
     Args:
-        args (Tuple[np.ndarray, int, tuple, int]): Tuple of
+        args (tuple[np.ndarray, int, tuple, int]): Tuple of
             (samples, bins, ranges, dimension)
     """
     sample_, bins_, ranges_, _ = args
@@ -316,18 +315,18 @@ def test_bin_edges_to_bin_centers() -> None:
     ],
 )
 def test_simplify_binning_arguments(
-    args: Tuple[List[int], List[str], List[Tuple[float, float]]],
+    args: tuple[list[int], list[str], list[tuple[float, float]]],
     arg_type: str,
 ) -> None:
     """Test the result of the _simplify_binning_arguments functions for number of
     bins and ranges
     """
-    bins_: Union[int, list, dict] = None
-    axes_: List[str] = None
-    ranges_: List[Tuple[float, float]] = None
-    bins_expected: List[Any] = None
-    axes_expected: List[Any] = None
-    ranges_expected: List[Any] = None
+    bins_: int | list | dict = None
+    axes_: list[str] = None
+    ranges_: list[tuple[float, float]] = None
+    bins_expected: list[Any] = None
+    axes_expected: list[Any] = None
+    ranges_expected: list[Any] = None
 
     bin_centers = []
     for i in range(len(args[1])):
diff --git a/tests/test_config.py b/tests/test_config.py
index 79e41ac4..6753c2a1 100644
--- a/tests/test_config.py
+++ b/tests/test_config.py
@@ -1,9 +1,10 @@
 """This is a code that performs several tests for the settings loader.
 """
+from __future__ import annotations
+
 import copy
 import os
 import tempfile
-from importlib.util import find_spec
 from pathlib import Path
 
 import pytest
@@ -11,9 +12,14 @@
 from sed.core.config import complete_dictionary
 from sed.core.config import load_config
 from sed.core.config import parse_config
+from sed.core.config import read_env_var
 from sed.core.config import save_config
+from sed.core.config import save_env_var
+
+test_dir = os.path.dirname(__file__)
+test_config_dir = Path(f"{test_dir}/data/loader/")
+config_paths = test_config_dir.glob("*/*.yaml")
 
-package_dir = os.path.dirname(find_spec("sed").origin)
 default_config_keys = [
     "binning",
     "histogram",
@@ -34,7 +40,7 @@
 
 def test_default_config() -> None:
     """Test the config loader for the default config."""
-    config = parse_config()
+    config = parse_config(config={}, folder_config={}, user_config={}, system_config={})
     assert isinstance(config, dict)
     for key in default_config_keys:
         assert key in config.keys()
@@ -47,7 +53,7 @@ def test_default_config() -> None:
 def test_load_dict() -> None:
     """Test the config loader for a dict."""
     config_dict = {"test_entry": True}
-    config = parse_config(config_dict)
+    config = parse_config(config_dict, verify_config=False)
     assert isinstance(config, dict)
     for key in default_config_keys:
         assert key in config.keys()
@@ -67,7 +73,14 @@ def test_load_does_not_modify() -> None:
     default_dict = {"a": 1, "b": {"c": 13}, "c": {"e": 11}}
     default_copy = copy.deepcopy(default_dict)
 
-    parse_config(config_dict, folder_dict, user_dict, system_dict, default_dict)
+    parse_config(
+        config_dict,
+        folder_dict,
+        user_dict,
+        system_dict,
+        default_dict,
+        verify_config=False,
+    )
     assert config_dict == config_copy
     assert folder_dict == folder_copy
     assert user_dict == user_copy
@@ -78,10 +91,10 @@ def test_load_does_not_modify() -> None:
 def test_load_config() -> None:
     """Test if the config loader can handle json and yaml files."""
     config_json = load_config(
-        f"{package_dir}/../tests/data/config/config.json",
+        f"{test_dir}/data/config/config.json",
     )
     config_yaml = load_config(
-        f"{package_dir}/../tests/data/config/config.yaml",
+        f"{test_dir}/data/config/config.yaml",
     )
     assert config_json == config_yaml
 
@@ -89,7 +102,7 @@ def test_load_config() -> None:
 def test_load_config_raise() -> None:
     """Test if the config loader raises an error for a wrong file type."""
     with pytest.raises(TypeError):
-        load_config(f"{package_dir}/../README.md")
+        load_config(f"{test_dir}/../README.md")
 
 
 def test_complete_dictionary() -> None:
@@ -131,3 +144,228 @@ def test_save_dict() -> None:
             save_config(config_dict, filename, overwrite=True)
             config = load_config(filename)
             assert "test_entry" not in config.keys()
+
+
+@pytest.mark.parametrize("config_path", config_paths)
+def test_config_model_valid(config_path) -> None:
+    """Test the config model for a valid config."""
+    config = parse_config(
+        config_path,
+        folder_config={},
+        user_config={},
+        system_config={},
+        verify_config=True,
+    )
+    assert config is not None
+
+
+def test_invalid_config_extra_field():
+    """Test that an invalid config with an extra field fails validation."""
+    default_config = parse_config(
+        folder_config={},
+        user_config={},
+        system_config={},
+        verify_config=True,
+    )
+    invalid_config = default_config.copy()
+    invalid_config["extra_field"] = "extra_value"
+    with pytest.raises(ValueError):
+        parse_config(
+            invalid_config,
+            folder_config={},
+            user_config={},
+            system_config={},
+            verify_config=True,
+        )
+
+
+def test_invalid_config_missing_field():
+    """Test that an invalid config with a missing required field fails validation."""
+    default_config = parse_config(
+        folder_config={},
+        user_config={},
+        system_config={},
+        verify_config=True,
+    )
+    invalid_config = default_config.copy()
+    del invalid_config["core"]["loader"]
+    with pytest.raises(ValueError):
+        parse_config(
+            folder_config={},
+            user_config={},
+            system_config={},
+            default_config=invalid_config,
+            verify_config=True,
+        )
+
+
+def test_invalid_config_wrong_values():
+    """Test that the validators for certain fields fails validation if not fulfilled."""
+    default_config = parse_config(
+        folder_config={},
+        user_config={},
+        system_config={},
+        verify_config=True,
+    )
+    invalid_config = default_config.copy()
+    invalid_config["core"]["loader"] = "nonexistent"
+    with pytest.raises(ValueError) as e:
+        parse_config(
+            folder_config={},
+            user_config={},
+            system_config={},
+            default_config=invalid_config,
+            verify_config=True,
+        )
+    assert "Invalid loader nonexistent. Available loaders are:" in str(e.value)
+    invalid_config = default_config.copy()
+    invalid_config["core"]["copy_tool"] = {}
+    invalid_config["core"]["copy_tool"]["source"] = "./"
+    invalid_config["core"]["copy_tool"]["dest"] = "./"
+    invalid_config["core"]["copy_tool"]["gid"] = 9999
+    with pytest.raises(ValueError) as e:
+        parse_config(
+            folder_config={},
+            user_config={},
+            system_config={},
+            default_config=invalid_config,
+            verify_config=True,
+        )
+    assert "Invalid value 9999 for gid. Group not found." in str(e.value)
+
+
+@pytest.fixture
+def mock_env_file(tmp_path, monkeypatch):
+    """Mock the .env file for testing"""
+    monkeypatch.setattr("sed.core.config.USER_CONFIG_PATH", tmp_path)
+    yield tmp_path
+
+
+def test_env_var_read_write(mock_env_file):  # noqa: ARG001
+    """Test reading and writing environment variables."""
+    # Test writing a new variable
+    save_env_var("TEST_VAR", "test_value")
+    assert read_env_var("TEST_VAR") == "test_value"
+
+    # Test writing multiple variables
+    save_env_var("TEST_VAR2", "test_value2")
+    assert read_env_var("TEST_VAR") == "test_value"
+    assert read_env_var("TEST_VAR2") == "test_value2"
+
+    # Test overwriting an existing variable
+    save_env_var("TEST_VAR", "new_value")
+    assert read_env_var("TEST_VAR") == "new_value"
+    assert read_env_var("TEST_VAR2") == "test_value2"  # Other variables unchanged
+
+    # Test reading non-existent variable
+    assert read_env_var("NON_EXISTENT_VAR") is None
+
+
+def test_env_var_read_no_file(mock_env_file):  # noqa: ARG001
+    """Test reading environment variables when .env file doesn't exist."""
+    # Test reading from non-existent file
+    assert read_env_var("TEST_VAR") is None
+
+
+def test_env_var_special_characters(mock_env_file):  # noqa: ARG001
+    """Test reading and writing environment variables with special characters."""
+    test_cases = {
+        "TEST_URL": "http://example.com/path?query=value",
+        "TEST_PATH": "/path/to/something/with/spaces and special=chars",
+        "TEST_QUOTES": "value with 'single' and \"double\" quotes",
+    }
+
+    for var_name, value in test_cases.items():
+        save_env_var(var_name, value)
+        assert read_env_var(var_name) == value
+
+
+def test_env_var_precedence(mock_env_file, tmp_path, monkeypatch):  # noqa: ARG001
+    """Test that environment variables are read in correct order of precedence"""
+    # Create local .env directory if it doesn't exist
+    local_env_dir = tmp_path / "local"
+    local_env_dir.mkdir(exist_ok=True)
+    system_env_dir = tmp_path / "system"
+    system_env_dir.mkdir(exist_ok=True)
+    monkeypatch.setattr("sed.core.config.ENV_DIR", local_env_dir / ".env")
+    monkeypatch.setattr("sed.core.config.SYSTEM_CONFIG_PATH", system_env_dir)
+
+    # Set up test values in different locations
+    os.environ["TEST_VAR"] = "os_value"
+
+    # Save to system config first (4th precedence)
+    with open(system_env_dir / ".env", "w") as f:
+        f.write("TEST_VAR=system_value\n")
+
+    # Save to user config first (3rd precedence)
+    save_env_var("TEST_VAR", "user_value")
+
+    # Create local .env file (2nd precedence)
+    with open(local_env_dir / ".env", "w") as f:
+        f.write("TEST_VAR=local_value\n")
+
+    assert read_env_var("TEST_VAR") == "os_value"
+
+    # Remove from OS env to test other precedence levels
+    monkeypatch.delenv("TEST_VAR", raising=False)
+    assert read_env_var("TEST_VAR") == "local_value"
+
+    # Remove local .env and should get user config value
+    (local_env_dir / ".env").unlink()
+    assert read_env_var("TEST_VAR") == "user_value"
+
+    # Remove user config and should get system value
+    (mock_env_file / ".env").unlink()
+    assert read_env_var("TEST_VAR") == "system_value"
+
+    # Remove system config and should get None
+    (system_env_dir / ".env").unlink()
+    assert read_env_var("TEST_VAR") is None
+
+
+def test_env_var_save_and_load(mock_env_file, monkeypatch):  # noqa: ARG001
+    """Test saving and loading environment variables"""
+    # Clear any existing OS environment variables
+    monkeypatch.delenv("TEST_VAR", raising=False)
+    monkeypatch.delenv("OTHER_VAR", raising=False)
+
+    # Save a variable
+    save_env_var("TEST_VAR", "test_value")
+
+    # Should be able to read it back
+    assert read_env_var("TEST_VAR") == "test_value"
+
+    # Save another variable - should preserve existing ones
+    save_env_var("OTHER_VAR", "other_value")
+    assert read_env_var("TEST_VAR") == "test_value"
+    assert read_env_var("OTHER_VAR") == "other_value"
+
+
+def test_env_var_not_found(mock_env_file):  # noqa: ARG001
+    """Test behavior when environment variable is not found"""
+    assert read_env_var("NONEXISTENT_VAR") is None
+
+
+def test_env_file_format(mock_env_file, monkeypatch):  # noqa: ARG001
+    """Test that .env file parsing handles different formats correctly"""
+    # Clear any existing OS environment variables
+    monkeypatch.delenv("TEST_VAR", raising=False)
+    monkeypatch.delenv("SPACES_VAR", raising=False)
+    monkeypatch.delenv("EMPTY_VAR", raising=False)
+    monkeypatch.delenv("COMMENT", raising=False)
+
+    with open(mock_env_file / ".env", "w") as f:
+        f.write(
+            """
+                TEST_VAR=value1
+                SPACES_VAR  =  value2
+                EMPTY_VAR=
+                #COMMENT=value3
+                INVALID_LINE
+                """,
+        )
+
+    assert read_env_var("TEST_VAR") == "value1"
+    assert read_env_var("SPACES_VAR") == "value2"
+    assert read_env_var("EMPTY_VAR") == ""
+    assert read_env_var("COMMENT") is None
diff --git a/tests/test_dataset.py b/tests/test_dataset.py
index 5082533e..e97a2764 100644
--- a/tests/test_dataset.py
+++ b/tests/test_dataset.py
@@ -1,3 +1,7 @@
+"""This code  performs several tests for the dataset module.
+"""
+from __future__ import annotations
+
 import io
 import json
 import os
@@ -11,7 +15,7 @@
 from sed.dataset import DatasetsManager as dm
 
 package_dir = os.path.dirname(find_spec("sed").origin)
-json_path = os.path.join(package_dir, "datasets.json")
+json_path = os.path.join(package_dir, "config/datasets.json")
 
 
 @pytest.fixture
@@ -116,7 +120,7 @@ def test_rearrange_data(zip_file):  # noqa: ARG001
     ds._rearrange_data()
     assert os.path.exists("test/datasets/Test/test_file.txt")
     assert os.path.exists("test/datasets/Test/test_subdir.txt")
-    assert ~os.path.exists("test/datasets/Test/subdir")
+    assert not os.path.exists("test/datasets/Test/subdir")
 
     with pytest.raises(FileNotFoundError):
         ds._subdirs = ["non_existing_subdir"]
diff --git a/tests/test_dfops.py b/tests/test_dfops.py
index 2ce894dc..3a6482cd 100644
--- a/tests/test_dfops.py
+++ b/tests/test_dfops.py
@@ -1,8 +1,9 @@
 """This file contains code that performs several tests for the dfops functions
 """
+from __future__ import annotations
+
 import datetime as dt
 from typing import Any
-from typing import List
 
 import dask.dataframe as ddf
 import numpy as np
@@ -343,7 +344,7 @@ def test_offset_by_other_columns_functionality() -> None:
         offset_columns=["off1"],
         weights=[1],
     )
-    expected: List[Any] = [11, 22, 33, 44, 55, 66]
+    expected: list[Any] = [11, 22, 33, 44, 55, 66]
     np.testing.assert_allclose(res["target"].values, expected)
 
     res = offset_by_other_columns(
diff --git a/tests/test_diagnostics.py b/tests/test_diagnostics.py
index ade40db9..24223460 100644
--- a/tests/test_diagnostics.py
+++ b/tests/test_diagnostics.py
@@ -1,9 +1,10 @@
 """Module tests.diagnostics, tests for the sed.diagnostics module
 """
+from __future__ import annotations
+
 import glob
 import itertools
 import os
-from importlib.util import find_spec
 
 import pytest
 
@@ -11,12 +12,16 @@
 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/"
+test_dir = os.path.dirname(__file__)
+df_folder = f"{test_dir}/data/loader/mpes/"
+calibration_folder = f"{test_dir}/data/calibrator/"
 files = glob.glob(df_folder + "*.h5")
-config = parse_config(package_dir + "/../tests/data/config/config.yaml")
+config = parse_config(
+    f"{test_dir}/data/loader/mpes/config.yaml",
+    folder_config={},
+    user_config={},
+    system_config={},
+)
 loader = get_loader("mpes", config=config)
 
 
@@ -37,6 +42,10 @@ def test_plot_histogram(ncols: int, backend: str) -> None:
     bins = config["histogram"]["bins"]
     for loc, axis in enumerate(axes):
         if axis.startswith("@"):
-            axes[loc] = config["dataframe"].get(axis.strip("@"))
+            axes[loc] = config["dataframe"]["columns"].get(axis.strip("@"))
     values = {axis: dataframe[axis].compute() for axis in axes}
     grid_histogram(values, ncols, axes, bins, ranges, backend)
+
+    # illegal keywords:
+    with pytest.raises(TypeError):
+        grid_histogram(values, ncols, axes, bins, ranges, backend, illegal_kwd=True)
diff --git a/tests/test_io.py b/tests/test_io.py
index 0f5f7abf..5725ab47 100644
--- a/tests/test_io.py
+++ b/tests/test_io.py
@@ -1,5 +1,7 @@
 """This file contains code that performs several tests for the input/output functions
 """
+from __future__ import annotations
+
 import os
 import random
 from pathlib import Path
diff --git a/tests/test_metadata.py b/tests/test_metadata.py
index fbe979a4..80672d0d 100644
--- a/tests/test_metadata.py
+++ b/tests/test_metadata.py
@@ -1,6 +1,9 @@
+"""This code  performs several tests for the metadata handler module.
+"""
+from __future__ import annotations
+
 import json
 from typing import Any
-from typing import Dict
 
 import numpy as np
 import pytest
@@ -8,7 +11,7 @@
 from sed.core.metadata import DuplicateEntryError
 from sed.core.metadata import MetaHandler
 
-metadata: Dict[Any, Any] = {}
+metadata: dict[Any, Any] = {}
 metadata["entry_title"] = "Title"
 # sample
 metadata["sample"] = {}
diff --git a/tests/test_processor.py b/tests/test_processor.py
index 67d6bcd1..3cfe9bcf 100644
--- a/tests/test_processor.py
+++ b/tests/test_processor.py
@@ -1,5 +1,7 @@
 """Module tests.processor, tests for the sed.core.processor module
 """
+from __future__ import annotations
+
 import csv
 import glob
 import itertools
@@ -10,9 +12,6 @@
 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
@@ -25,29 +24,29 @@
 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/mpes/"
-df_folder_generic = package_dir + "/../tests/data/loader/generic/"
-folder = package_dir + "/../tests/data/calibrator/"
+test_dir = os.path.dirname(__file__)
+df_folder = f"{test_dir}/data/loader/mpes/"
+df_folder_generic = f"{test_dir}/data/loader/generic/"
+calibration_folder = f"{test_dir}/data/calibrator/"
 files = glob.glob(df_folder + "*.h5")
 runs = ["30", "50"]
 runs_flash = ["43878", "43878"]
 loader = get_loader(loader_name="mpes")
-source_folder = package_dir + "/../"
+source_folder = f"{test_dir}/../"
 dest_folder = tempfile.mkdtemp()
 gid = os.getgid()
 
 traces_list = []
-with open(folder + "traces.csv", newline="", encoding="utf-8") as csvfile:
+with open(calibration_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:
+with open(calibration_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:
+with open(calibration_folder + "biases.csv", newline="", encoding="utf-8") as csvfile:
     reader = csv.reader(csvfile, quoting=csv.QUOTE_NONNUMERIC)
     biases = np.asarray(next(reader))
 
@@ -151,6 +150,19 @@ def test_additional_parameter_to_loader() -> None:
     )
     assert processor.files[0].find("json") > -1
 
+    # check that illegal keywords raise:
+    with pytest.raises(TypeError):
+        processor = SedProcessor(
+            folder=df_folder_generic,
+            ftype="json",
+            config=config,
+            folder_config={},
+            user_config={},
+            system_config={},
+            verbose=True,
+            illegal_keyword=True,
+        )
+
 
 def test_repr() -> None:
     """test the ___repr___ method"""
@@ -171,6 +183,9 @@ def test_repr() -> None:
     assert processor_str.find("ADC") > 0
     assert processor_str.find("key1") > 0
 
+    with pytest.raises(TypeError):
+        processor.load(files=files, metadata={"test": {"key1": "value1"}}, illegal_keyword=True)
+
 
 def test_attributes_setters() -> None:
     """Test class attributes and setters."""
@@ -204,7 +219,7 @@ def test_attributes_setters() -> None:
 
 def test_copy_tool() -> None:
     """Test the copy tool functionality in the processor"""
-    config = {"core": {"loader": "mpes", "use_copy_tool": True}}
+    config: dict[str, dict[str, Any]] = {"core": {"loader": "mpes"}}
     processor = SedProcessor(
         config=config,
         folder_config={},
@@ -216,10 +231,7 @@ def test_copy_tool() -> None:
     config = {
         "core": {
             "loader": "mpes",
-            "use_copy_tool": True,
-            "copy_tool_source": source_folder,
-            "copy_tool_dest": dest_folder,
-            "copy_tool_kwds": {"gid": os.getgid()},
+            "copy_tool": {"source": source_folder, "dest": dest_folder, "gid": os.getgid()},
         },
     }
     processor = SedProcessor(
@@ -261,7 +273,7 @@ def test_copy_tool() -> None:
 )
 feature_list = [feature4, feature5, feature6, feature7]
 
-adjust_params = {
+adjust_params: dict[str, Any] = {
     "scale": np.random.randint(1, 10) / 10 + 0.5,
     "xtrans": np.random.randint(1, 50),
     "ytrans": np.random.randint(1, 50),
@@ -337,11 +349,11 @@ def test_pose_adjustment() -> None:
         verbose=True,
     )
     # pose adjustment w/o loaded image
-    processor.pose_adjustment(**adjust_params, use_correction=False, apply=True)  # type: ignore
+    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)  # type: ignore
+    processor.pose_adjustment(**adjust_params, use_correction=False, apply=True)
 
     processor = SedProcessor(
         folder=df_folder,
@@ -361,11 +373,15 @@ def test_pose_adjustment() -> None:
         apply=True,
     )
     processor.generate_splinewarp(use_center=True)
-    processor.pose_adjustment(**adjust_params, apply=True)  # type: ignore[arg-type]
+    processor.pose_adjustment(**adjust_params, apply=True)
     processor.apply_momentum_correction()
     assert "Xm" in processor.dataframe.columns
     assert "Ym" in processor.dataframe.columns
 
+    # illegal keywords:
+    with pytest.raises(TypeError):
+        processor.pose_adjustment(**adjust_params, apply=True, illegal_kwd=True)
+
 
 def test_pose_adjustment_save_load() -> None:
     """Test for the saving and loading of pose correction and application of momentum correction
@@ -589,7 +605,7 @@ def test_energy_calibration_workflow(energy_scale: str, calibration_method: str)
     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, ...]] = [
+    ranges: list[tuple[Any, ...]] = [
         (64638.0, 65386.0),
         (64913.0, 65683.0),
         (65188.0, 65991.0),
@@ -609,18 +625,15 @@ def test_energy_calibration_workflow(energy_scale: str, calibration_method: str)
     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,
     )
@@ -662,8 +675,9 @@ def test_align_dld_sectors() -> None:
         user_config={},
         system_config={},
     )
-    config["core"]["paths"]["data_parquet_dir"] = (
-        config["core"]["paths"]["data_parquet_dir"] + "_align_dld_sectors"
+    config["core"]["paths"]["processed"] = Path(
+        config["core"]["paths"]["processed"],
+        "_align_dld_sectors",
     )
     processor = SedProcessor(
         folder=df_folder + "../flash/",
@@ -678,7 +692,6 @@ def test_align_dld_sectors() -> None:
     assert "dldSectorID" in processor.dataframe.columns
 
     sector_delays = np.asarray([10, -10, 20, -20, 30, -30, 40, -40])
-
     tof_ref = []
     for i in range(len(sector_delays)):
         tof_ref.append(
@@ -705,7 +718,7 @@ def test_align_dld_sectors() -> None:
     np.testing.assert_allclose(tof_ref_array, tof_aligned_array + sector_delays[:, np.newaxis])
 
     # cleanup flash intermediaries
-    parquet_data_dir = config["core"]["paths"]["data_parquet_dir"]
+    parquet_data_dir = config["core"]["paths"]["processed"]
     for file in os.listdir(Path(parquet_data_dir, "buffer")):
         os.remove(Path(parquet_data_dir, "buffer", file))
 
@@ -722,7 +735,7 @@ def test_append_tof_ns_axis() -> None:
         verbose=True,
     )
     processor.append_tof_ns_axis()
-    assert processor.config["dataframe"]["tof_ns_column"] in processor.dataframe
+    assert processor.config["dataframe"]["columns"]["tof_ns"] in processor.dataframe
 
 
 def test_delay_calibration_workflow() -> None:
@@ -852,6 +865,7 @@ def test_add_time_stamped_data() -> None:
         system_config={},
         time_stamps=True,
         verbose=True,
+        verify_config=False,
     )
     df_ts = processor.dataframe.timeStamps.compute().values
     data = np.linspace(0, 1, 20)
@@ -907,6 +921,10 @@ def test_compute() -> None:
     assert result.data.shape == tuple(bins)
     assert result.data.sum(axis=(0, 1, 2, 3)) > 0
 
+    # illegal keywords:
+    with pytest.raises(TypeError):
+        processor.compute(illegal_kwd=True)
+
 
 def test_compute_with_filter() -> None:
     """Test binning of final result using filters"""
@@ -1004,7 +1022,7 @@ def test_compute_with_normalization() -> None:
 
 def test_get_normalization_histogram() -> None:
     """Test the generation function for the normalization histogram"""
-    config = {"core": {"loader": "mpes"}, "dataframe": {"time_stamp_alias": "timeStamps"}}
+    config = {"core": {"loader": "mpes"}, "dataframe": {"columns": {"timestamp": "timeStamps"}}}
     processor = SedProcessor(
         folder=df_folder,
         config=config,
@@ -1038,8 +1056,12 @@ def test_get_normalization_histogram() -> None:
     # histogram2 = processor.get_normalization_histogram(axis="ADC", use_time_stamps="True")
     # np.testing.assert_allclose(histogram1, histogram2)
 
+    # illegal keywords:
+    with pytest.raises(TypeError):
+        histogram1 = processor.get_normalization_histogram(axis="ADC", illegal_kwd=True)
+
 
-metadata: Dict[Any, Any] = {}
+metadata: dict[Any, Any] = {}
 metadata["entry_title"] = "Title"
 # user
 metadata["user0"] = {}
@@ -1068,14 +1090,16 @@ def test_save(caplog) -> None:
     config = parse_config(
         config={"dataframe": {"tof_binning": 1}},
         folder_config={},
-        user_config=package_dir + "/../sed/config/mpes_example_config.yaml",
+        user_config=package_dir + "/config/mpes_example_config.yaml",
         system_config={},
+        verify_config=False,
     )
     config["metadata"]["lens_mode_config"]["6kV_kmodem4.0_30VTOF_453ns_focus.sav"][
         "MCPfront"
     ] = 21.0
     config["metadata"]["lens_mode_config"]["6kV_kmodem4.0_30VTOF_453ns_focus.sav"]["Z1"] = 2450
     config["metadata"]["lens_mode_config"]["6kV_kmodem4.0_30VTOF_453ns_focus.sav"]["F"] = 69.23
+    config["nexus"]["input_files"] = [package_dir + "/config/NXmpes_config.json"]
     processor = SedProcessor(
         folder=df_folder,
         config=config,
@@ -1107,7 +1131,6 @@ def test_save(caplog) -> None:
     # and error if any validation problems occur.
     processor.save(
         "output.nxs",
-        input_files=df_folder + "../../../../sed/config/NXmpes_config.json",
         fail=True,
     )
     assert os.path.isfile("output.nxs")
@@ -1116,7 +1139,6 @@ def test_save(caplog) -> None:
     with pytest.raises(ValidationFailed):
         processor.save(
             "result.nxs",
-            input_files=df_folder + "../../../../sed/config/NXmpes_config.json",
             fail=True,
         )
     # Check that the issues are raised as warnings per default:
@@ -1125,7 +1147,7 @@ def test_save(caplog) -> None:
         yaml.dump({"Instrument": {"undocumented_field": "undocumented entry"}}, f)
     with open("temp_config.json", "w") as f:
         with open(
-            df_folder + "../../../../sed/config/NXmpes_config.json",
+            package_dir + "/config/NXmpes_config.json",
             encoding="utf-8",
         ) as stream:
             config_dict = json.load(stream)
diff --git a/tutorial/10_hextof_workflow_trXPS_bam_correction.ipynb b/tutorial/10_hextof_workflow_trXPS_bam_correction.ipynb
index 1b7e0d54..3f4c8630 100644
--- a/tutorial/10_hextof_workflow_trXPS_bam_correction.ipynb
+++ b/tutorial/10_hextof_workflow_trXPS_bam_correction.ipynb
@@ -96,7 +96,7 @@
    "outputs": [],
    "source": [
     "# pick the default configuration file for hextof@FLASH\n",
-    "config_file = Path('../sed/config/flash_example_config.yaml')\n",
+    "config_file = Path('../src/sed/config/flash_example_config.yaml')\n",
     "assert config_file.exists()"
    ]
   },
@@ -112,8 +112,8 @@
     "    \"core\": {\n",
     "        \"beamtime_id\": 11019101,\n",
     "        \"paths\": {\n",
-    "            \"data_raw_dir\": path,\n",
-    "            \"data_parquet_dir\": buffer_path\n",
+    "            \"raw\": path,\n",
+    "            \"processed\": buffer_path\n",
     "        },\n",
     "    },\n",
     "}"
@@ -129,26 +129,24 @@
     "energy_cal = {\n",
     "    \"energy\": {\n",
     "        \"calibration\": {\n",
-    "            \"E0\": -53.96145014592986,\n",
-    "            \"creation_date\": 1732056868.029444,\n",
-    "            \"d\": 0.8096677233434938,\n",
+    "            \"E0\": -132.47100427179566,\n",
+    "            \"creation_date\": '2024-11-30T20:47:03.305244',\n",
+    "            \"d\": 0.8096677238144319,\n",
     "            \"energy_scale\": \"kinetic\",\n",
-    "            \"t0\": 4.0148196718030886e-07,\n",
+    "            \"t0\": 4.0148196706891397e-07,\n",
     "        },\n",
     "        \"offsets\":{\n",
-    "            \"constant\": -77.5,\n",
-    "            \"creation_date\": 1732056874.060922,\n",
-    "            \"monochromatorPhotonEnergy\": {\n",
-    "                \"preserve_mean\": True,\n",
-    "                \"weight\": -1,\n",
-    "            },\n",
-    "            \"sampleBias\": {\n",
-    "                \"preserve_mean\": False,\n",
-    "                \"weight\": 1,\n",
-    "            },\n",
-    "            \"tofVoltage\": {\n",
-    "                \"preserve_mean\": True,\n",
-    "                \"weight\": -1,\n",
+    "            \"constant\": 1,\n",
+    "            \"creation_date\": '2024-11-30T21:17:07.762199',\n",
+    "            \"columns\": {\n",
+    "                \"monochromatorPhotonEnergy\": {\n",
+    "                    \"preserve_mean\": True,\n",
+    "                    \"weight\": -1,\n",
+    "                },\n",
+    "                \"tofVoltage\": {\n",
+    "                    \"preserve_mean\": True,\n",
+    "                    \"weight\": -1,\n",
+    "                },\n",
     "            },\n",
     "        },\n",
     "    },\n",
@@ -535,11 +533,19 @@
    "metadata": {},
    "outputs": [],
    "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e2e6e852",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "python3",
+   "display_name": "sed-processor-7Jy-bAA8-py3.9",
    "language": "python",
    "name": "python3"
   },
diff --git a/tutorial/11_hextof_workflow_trXPS_energy_calibration_using_SB.ipynb b/tutorial/11_hextof_workflow_trXPS_energy_calibration_using_SB.ipynb
index 3c89547c..4d3fbe40 100644
--- a/tutorial/11_hextof_workflow_trXPS_energy_calibration_using_SB.ipynb
+++ b/tutorial/11_hextof_workflow_trXPS_energy_calibration_using_SB.ipynb
@@ -97,7 +97,7 @@
    "outputs": [],
    "source": [
     "# pick the default configuration file for hextof@FLASH\n",
-    "config_file = Path('../sed/config/flash_example_config.yaml')\n",
+    "config_file = Path('../src/sed/config/flash_example_config.yaml')\n",
     "assert config_file.exists()"
    ]
   },
@@ -113,8 +113,8 @@
     "    \"core\": {\n",
     "        \"beamtime_id\": 11019101,\n",
     "        \"paths\": {\n",
-    "            \"data_raw_dir\": path,\n",
-    "            \"data_parquet_dir\": buffer_path\n",
+    "            \"raw\": path,\n",
+    "            \"processed\": buffer_path\n",
     "        },\n",
     "    },\n",
     "}"
@@ -175,14 +175,6 @@
     "sp_44455.find_bias_peaks(ranges=ranges, ref_id=ref_id, apply=True)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "d3e4f30d",
-   "metadata": {},
-   "source": [
-    "We offset the reference energy by the different in bias voltages between this run and run 44498"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -191,14 +183,12 @@
    "outputs": [],
    "source": [
     "sp_44455.calibrate_energy_axis(\n",
-    "    ref_id=0,\n",
-    "    ref_energy=-34.9,\n",
+    "    ref_energy=-31.4,\n",
     "    method=\"lmfit\",\n",
     "    energy_scale='kinetic',\n",
     "    d={'value':1.0,'min': .7, 'max':1.0, 'vary':True},\n",
     "    t0={'value':5e-7, 'min': 1e-7, 'max': 1e-6, 'vary':True},\n",
     "    E0={'value': 0., 'min': -200, 'max': 100, 'vary': True},\n",
-    "    verbose=True,\n",
     ")"
    ]
   },
@@ -225,7 +215,8 @@
    "id": "98266c62-ab48-4746-96c8-2d47cf92c0e9",
    "metadata": {},
    "source": [
-    "### Now we can use those parameters and load our trXPS data using the additional config file"
+    "### Now we can use those parameters and load our trXPS data using the additional config file\n",
+    "To obtain a correct energy axis, we offset the energy axis by the difference of photon energy between this run and the energy calibration runs"
    ]
   },
   {
@@ -238,7 +229,13 @@
     "run_number = 44498\n",
     "sp_44498 = SedProcessor(runs=[run_number], config=config_override, folder_config=\"reference_calib.yaml\", system_config=config_file, verbose=True)\n",
     "sp_44498.add_jitter()\n",
-    "sp_44498.append_energy_axis()"
+    "sp_44498.append_energy_axis()\n",
+    "sp_44498.add_energy_offset(\n",
+    "    constant=1,\n",
+    "    columns=['monochromatorPhotonEnergy','tofVoltage'],\n",
+    "    weights=[-1,-1],\n",
+    "    preserve_mean=[True, True],\n",
+    ")"
    ]
   },
   {
@@ -332,10 +329,10 @@
     "\n",
     "fig,ax = plt.subplots(1,2,figsize=(8,3), layout='constrained')\n",
     "fig.suptitle(f\"Run {run_number}: W 4f, side bands\")\n",
-    "res_corr.plot(robust=True, ax=ax[0], cmap='terrain')\n",
+    "res_corr.plot(ax=ax[0], cmap='terrain')\n",
     "ax[0].set_title('raw')\n",
     "bg = res_corr.sel(delayStage=slice(-1.3,-1.0)).mean('delayStage')\n",
-    "(res_corr-bg).plot(robust=True, ax=ax[1])\n",
+    "(res_corr-bg).plot(ax=ax[1])\n",
     "ax[1].set_title('difference')"
    ]
   },
@@ -395,21 +392,18 @@
    "outputs": [],
    "source": [
     "### Kinetic energy of w4f peaks and their SB\n",
-    "ref_energy = -31.4\n",
-    "ref_id = 1\n",
+    "ref_energy = -30.2\n",
     "sp_44498.ec.biases = -1*np.array([-30.2,-31.4,-32.6,-33.6,-34.8])\n",
     "sp_44498.ec.peaks = np.expand_dims(data[peaks]['dldTimeSteps'].data,1)\n",
     "sp_44498.ec.tof = res_corr.dldTimeSteps.data\n",
     "\n",
     "sp_44498.calibrate_energy_axis(\n",
-    "    ref_id=ref_id,\n",
     "    ref_energy=ref_energy,\n",
     "    method=\"lmfit\",\n",
     "    d={'value':1.0,'min': .8, 'max':1.0, 'vary':True},\n",
     "    t0={'value':5e-7, 'min': 1e-7, 'max': 1e-6, 'vary':True},\n",
     "    E0={'value': -100., 'min': -200, 'max': 15, 'vary': True},\n",
-    "    labels=\"\",\n",
-    "    verbose=True)"
+    ")"
    ]
   },
   {
@@ -417,7 +411,8 @@
    "id": "4052d629-1178-4248-a945-d60a6ff34bf3",
    "metadata": {},
    "source": [
-    "### Append energy axis into a data frame, bin and visualize data in the calibrated energy and corrected delay axis "
+    "### Append energy axis into a data frame, bin and visualize data in the calibrated energy and corrected delay axis\n",
+    "To get a correct energy axis, we undo the shifts imposed by the calibration function"
    ]
   },
   {
@@ -427,7 +422,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sp_44498.append_energy_axis()"
+    "sp_44498.append_energy_axis()\n",
+    "sp_44498.add_energy_offset(\n",
+    "    constant=30.2,\n",
+    "    columns=['monochromatorPhotonEnergy','tofVoltage','sampleBias'],\n",
+    "    weights=[-1,-1,-1],\n",
+    "    preserve_mean=[True, True,False],\n",
+    ")"
    ]
   },
   {
@@ -444,10 +445,10 @@
     "\n",
     "fig,ax = plt.subplots(1,2,figsize=(8,3), layout='constrained')\n",
     "fig.suptitle(f\"Run {run_number}: W 4f, side bands\")\n",
-    "res_corr.plot(robust=True, ax=ax[0], cmap='terrain')\n",
+    "res_corr.plot(ax=ax[0], cmap='terrain')\n",
     "ax[0].set_title('raw')\n",
     "bg = res_corr.sel(delayStage=slice(-1.3,-1.0)).mean('delayStage')\n",
-    "(res_corr-bg).plot(robust=True, ax=ax[1])\n",
+    "(res_corr-bg).plot(ax=ax[1])\n",
     "ax[1].set_title('difference')"
    ]
   },
diff --git a/tutorial/2_conversion_pipeline_for_example_time-resolved_ARPES_data.ipynb b/tutorial/2_conversion_pipeline_for_example_time-resolved_ARPES_data.ipynb
index 6ee3d7c1..4074fc49 100644
--- a/tutorial/2_conversion_pipeline_for_example_time-resolved_ARPES_data.ipynb
+++ b/tutorial/2_conversion_pipeline_for_example_time-resolved_ARPES_data.ipynb
@@ -59,7 +59,7 @@
    "outputs": [],
    "source": [
     "# create sed processor using the config file:\n",
-    "sp = sed.SedProcessor(folder=scandir, config=\"../sed/config/mpes_example_config.yaml\", verbose=True)"
+    "sp = sed.SedProcessor(folder=scandir, config=\"../src/sed/config/mpes_example_config.yaml\", system_config={}, verbose=True)"
    ]
   },
   {
@@ -466,9 +466,9 @@
    "metadata": {},
    "source": [
     "#### 3. Step:\n",
-    "Next, the detected peak positions and bias voltages are used to determine the calibration function. This can be either done by fitting the functional form d^2/(t-t0)^2 via lmfit (\"lmfit\"), or using a polynomial approximation (\"lstsq\" or \"lsqr\"). Here, one can also define a reference id, and a reference energy. Those define the absolute energy position of the feature used for calibration in the \"reference\" trace, at the bias voltage where the final measurement has been performed. The energy scale can be either \"kinetic\" (decreasing energy with increasing TOF), or \"binding\" (increasing energy with increasing TOF).\n",
+    "Next, the detected peak positions and bias voltages are used to determine the calibration function. Essentially, the functional Energy(TOF) is being determined by either least-squares fitting of the functional form d^2/(t-t0)^2 via lmfit (``method``: \"lmfit\"), or by analytically obtaining a polynomial approximation (``method``: \"lstsq\" or \"lsqr\"). The parameter ``ref_energy`` is used to define the absolute energy position of the feature used for calibration in the calibrated energy scale. ``energy_scale`` can be either \"kinetic\" (decreasing energy with increasing TOF), or \"binding\" (increasing energy with increasing TOF).\n",
     "\n",
-    "After calculating the calibration, all traces corrected with the calibration are plotted ontop of each other, the calibration function together with the extracted features is plotted."
+    "After calculating the calibration, all traces corrected with the calibration are plotted ontop of each other, and the calibration function (Energy(TOF)) together with the extracted features is being plotted."
    ]
   },
   {
@@ -478,21 +478,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# use the refid of the bias that the measurement was taken at\n",
-    "# Eref can be used to set the absolute energy (kinetic energy, E-EF) of the feature used for energy calibration (if known)\n",
-    "refid=4\n",
-    "Eref=-0.5\n",
+    "# Eref can be used to set the absolute energy (kinetic energy, E-EF, etc.) of the feature used for energy calibration (if known)\n",
+    "Eref=-1.3\n",
     "# the lmfit method uses a fit of (d/(t-t0))**2 to determine the energy calibration\n",
     "# limits and starting values for the fitting parameters can be provided as dictionaries\n",
     "sp.calibrate_energy_axis(\n",
-    "    ref_id=refid,\n",
     "    ref_energy=Eref,\n",
     "    method=\"lmfit\",\n",
     "    energy_scale='kinetic',\n",
     "    d={'value':1.0,'min': .7, 'max':1.2, 'vary':True},\n",
     "    t0={'value':8e-7, 'min': 1e-7, 'max': 1e-6, 'vary':True},\n",
     "    E0={'value': 0., 'min': -100, 'max': 0, 'vary': True},\n",
-    "    verbose=True,\n",
     ")"
    ]
   },
@@ -523,7 +519,7 @@
    "metadata": {},
    "source": [
     "#### 4. Step:\n",
-    "Finally, the the energy axis is added to the dataframe."
+    "Finally, the the energy axis is added to the dataframe. Here, the applied bias voltages of the measurement is taken into account to provide the correct energy offset. If the bias cannot be read from the file, it can be provided manually."
    ]
   },
   {
@@ -533,7 +529,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sp.append_energy_axis()"
+    "sp.append_energy_axis(bias_voltage=16.8)"
    ]
   },
   {
@@ -546,6 +542,16 @@
     "The delay axis is calculated from the ADC input column based on the provided delay range. ALternatively, the delay scan range can also be extracted from attributes inside a source file, if present."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1619cbc6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sp.dataframe.head()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -651,9 +657,6 @@
   }
  ],
  "metadata": {
-  "interpreter": {
-   "hash": "728003ee06929e5fa5ff815d1b96bf487266025e4b7440930c6bf4536d02d243"
-  },
   "kernelspec": {
    "display_name": "python3",
    "language": "python",
@@ -669,7 +672,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.19"
   }
  },
  "nbformat": 4,
diff --git a/tutorial/3_metadata_collection_and_export_to_NeXus.ipynb b/tutorial/3_metadata_collection_and_export_to_NeXus.ipynb
index 800e5836..d88d9d1f 100644
--- a/tutorial/3_metadata_collection_and_export_to_NeXus.ipynb
+++ b/tutorial/3_metadata_collection_and_export_to_NeXus.ipynb
@@ -143,7 +143,7 @@
    "outputs": [],
    "source": [
     "# create sed processor using the config file, and collect the meta data from the files:\n",
-    "sp = sed.SedProcessor(folder=scandir, config=\"../sed/config/mpes_example_config.yaml\", metadata=metadata, collect_metadata=True)"
+    "sp = sed.SedProcessor(folder=scandir, config=\"../src/sed/config/mpes_example_config.yaml\", system_config={}, metadata=metadata, collect_metadata=True)"
    ]
   },
   {
@@ -220,7 +220,7 @@
    "outputs": [],
    "source": [
     "# Apply stored config energy calibration\n",
-    "sp.append_energy_axis()"
+    "sp.append_energy_axis(bias_voltage=16.8)"
    ]
   },
   {
@@ -290,9 +290,6 @@
   }
  ],
  "metadata": {
-  "interpreter": {
-   "hash": "728003ee06929e5fa5ff815d1b96bf487266025e4b7440930c6bf4536d02d243"
-  },
   "kernelspec": {
    "display_name": "python3",
    "language": "python",
@@ -308,7 +305,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.19"
   }
  },
  "nbformat": 4,
diff --git a/tutorial/4_hextof_workflow.ipynb b/tutorial/4_hextof_workflow.ipynb
index 334671a9..52dcba7f 100644
--- a/tutorial/4_hextof_workflow.ipynb
+++ b/tutorial/4_hextof_workflow.ipynb
@@ -103,10 +103,17 @@
    "outputs": [],
    "source": [
     "# pick the default configuration file for hextof@FLASH\n",
-    "config_file = Path('../sed/config/flash_example_config.yaml')\n",
+    "config_file = Path('../src/sed/config/flash_example_config.yaml')\n",
     "assert config_file.exists()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The path to the processed folder can also be defined as a keyword argument later."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -117,8 +124,8 @@
     "config_override = {\n",
     "    \"core\": {\n",
     "        \"paths\": {\n",
-    "            \"data_raw_dir\": path,\n",
-    "            \"data_parquet_dir\": buffer_path,\n",
+    "            \"raw\": path,\n",
+    "            \"processed\": buffer_path,\n",
     "        },\n",
     "    },\n",
     "}"
@@ -162,7 +169,19 @@
    "metadata": {},
    "source": [
     "### Generate the Processor instance\n",
-    "this cell generates an instance of the `SedProcessor` class. It will be our workhorse for the entire workflow."
+    "this cell generates an instance of the `SedProcessor` class. It will be our workhorse for the entire workflow.\n",
+    "\n",
+    "#### Important note\n",
+    "The following extra arguments are available for FlashLoader. None of which are necessary to give but helpful to know.\n",
+    "- **force_recreate**: Probably the most useful. In case the config is changed, this allows to reduce the raw h5 files to the the intermediate parquet format again. Otherwise, the schema between the saved dataframe and config differs.\n",
+    "- **debug**: Setting this runs the reduction process in serial, so the errors are easier to find.\n",
+    "- **remove_invalid_files**: Sometimes some critical channels defined in the config are missing in some raw files. Setting this will make sure to ignore such files.\n",
+    "- **filter_timed_by_electron**: Defaults to True. When True, the timed dataframe will only\n",
+    "                contain data points where valid electron events were detected. When False, all\n",
+    "                timed data points are included regardless of electron detection (see https://github.com/OpenCOMPES/sed/issues/307)\n",
+    "- **processed_dir**: Location to save the reduced parquet files. \n",
+    "- **scicat_token**: Token from your scicat account.\n",
+    "- **detector**: '1Q' and '4Q' detector for example. Useful when there are separate raw files for each detector."
    ]
   },
   {
@@ -501,14 +520,12 @@
    "outputs": [],
    "source": [
     "sp.calibrate_energy_axis(\n",
-    "    ref_id=4,\n",
     "    ref_energy=-.55,\n",
     "    method=\"lmfit\",\n",
     "    energy_scale='kinetic',\n",
     "    d={'value':1.0,'min': .2, 'max':1.0, 'vary':False},\n",
     "    t0={'value':5e-7, 'min': 1e-7, 'max': 1e-6, 'vary':True},\n",
     "    E0={'value': 0., 'min': -100, 'max': 100, 'vary': True},\n",
-    "    verbose=True,\n",
     ")"
    ]
   },
@@ -581,7 +598,7 @@
    "metadata": {},
    "source": [
     "### correct offsets\n",
-    "The energy axis is now correct, but still the curves do not stack on each other as we are not compensating for the `sampleBias`. In the same way, we can compensate the photon energy (`monochromatorPhotonEnergy`) and the `tofVoltage` "
+    "The energy axis is now correct, taking the sample bias of the measurement into account. Additionally, we can compensate the photon energy (`monochromatorPhotonEnergy`) and the `tofVoltage`."
    ]
   },
   {
@@ -591,10 +608,9 @@
    "outputs": [],
    "source": [
     "sp.add_energy_offset(\n",
-    "    constant=-32, # Sample bias used as reference for energy calibration\n",
-    "    columns=['sampleBias','monochromatorPhotonEnergy','tofVoltage'],\n",
-    "    weights=[1,-1,-1],\n",
-    "    preserve_mean=[False, True, True],\n",
+    "    columns=['monochromatorPhotonEnergy','tofVoltage'],\n",
+    "    weights=[-1,-1],\n",
+    "    preserve_mean=[True, True],\n",
     ")"
    ]
   },
@@ -797,7 +813,7 @@
     "axes = ['energy','delayStage']\n",
     "bins = [100,150]\n",
     "delay_start,delay_stop=1462.00,1464.85\n",
-    "ranges = [[-3,2], [-1.15, 1.7]]\n",
+    "ranges = [[-3,2], [-1.1, 1.75]]\n",
     "res = sp.compute(bins=bins, axes=axes, ranges=ranges)"
    ]
   },
@@ -940,7 +956,7 @@
     "axes = ['energy','delayStage']\n",
     "bins = [100,150]\n",
     "delay_start,delay_stop=1462.00,1464.85\n",
-    "ranges = [[-5,2], [-1.15, 1.7]]\n",
+    "ranges = [[-5,2], [-1.1, 1.75]]\n",
     "res = sp.compute(bins=bins, axes=axes, ranges=ranges, normalize_to_acquisition_time=\"delayStage\")"
    ]
   },
@@ -1001,7 +1017,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.9",
+   "display_name": "python3",
    "language": "python",
    "name": "python3"
   },
diff --git a/tutorial/5_sxp_workflow.ipynb b/tutorial/5_sxp_workflow.ipynb
index fb2a2f12..4136e519 100644
--- a/tutorial/5_sxp_workflow.ipynb
+++ b/tutorial/5_sxp_workflow.ipynb
@@ -85,7 +85,7 @@
    "outputs": [],
    "source": [
     "# pick the default configuration file for SXP@XFEL\n",
-    "config_file = Path('../sed/config/sxp_example_config.yaml')\n",
+    "config_file = Path('../src/sed/config/sxp_example_config.yaml')\n",
     "assert config_file.exists()"
    ]
   },
@@ -99,8 +99,8 @@
     "config_override = {\n",
     "    \"core\": {\n",
     "        \"paths\": {\n",
-    "            \"data_raw_dir\": path,\n",
-    "            \"data_parquet_dir\": buffer_path,\n",
+    "            \"raw\": path,\n",
+    "            \"processed\": buffer_path,\n",
     "        },\n",
     "    },\n",
     "}"
@@ -403,7 +403,6 @@
    "outputs": [],
    "source": [
     "sp.calibrate_energy_axis(\n",
-    "    ref_id=5,\n",
     "    ref_energy=-33,\n",
     "    method=\"lmfit\",\n",
     "    energy_scale='kinetic',\n",
@@ -447,7 +446,14 @@
     "sp.load(runs=np.arange(58, 62))\n",
     "sp.add_jitter()\n",
     "sp.filter_column(\"pulseId\", max_value=756)\n",
-    "sp.append_energy_axis()"
+    "sp.append_energy_axis(bias_voltage=957)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we can bin as function fo energy and delay stage position"
    ]
   },
   {
diff --git a/tutorial/6_binning_with_time-stamped_data.ipynb b/tutorial/6_binning_with_time-stamped_data.ipynb
index 230386eb..2c948f37 100644
--- a/tutorial/6_binning_with_time-stamped_data.ipynb
+++ b/tutorial/6_binning_with_time-stamped_data.ipynb
@@ -68,7 +68,7 @@
    "outputs": [],
    "source": [
     "# create sed processor using the config file with time-stamps:\n",
-    "sp = sed.SedProcessor(folder=scandir, user_config=\"../sed/config/mpes_example_config.yaml\", time_stamps=True)"
+    "sp = sed.SedProcessor(folder=scandir, user_config=\"../src/sed/config/mpes_example_config.yaml\", system_config={}, time_stamps=True, verbose=True)"
    ]
   },
   {
@@ -157,12 +157,12 @@
    "source": [
     "# Load energy calibration EDCs\n",
     "scans = np.arange(127,136)\n",
-    "voltages = np.arange(22,13,-1)\n",
+    "voltages = np.arange(21,12,-1)\n",
     "files = [caldir + r'/Scan' + str(num).zfill(4) + '_1.h5' for num in scans]\n",
     "sp.load_bias_series(data_files=files, normalize=True, biases=voltages, ranges=[(64000, 76000)])\n",
     "rg = (65500, 66000)\n",
     "sp.find_bias_peaks(ranges=rg, ref_id=5, infer_others=True, apply=True)\n",
-    "sp.calibrate_energy_axis(ref_energy=-0.5, ref_id=4, energy_scale=\"kinetic\", method=\"lmfit\")"
+    "sp.calibrate_energy_axis(ref_energy=-0.5, energy_scale=\"kinetic\", method=\"lmfit\")"
    ]
   },
   {
@@ -173,6 +173,7 @@
    "outputs": [],
    "source": [
     "# Apply stored config energy calibration\n",
+    "#sp.append_energy_axis(bias_voltage=17)\n",
     "sp.append_energy_axis()"
    ]
   },
diff --git a/tutorial/7_correcting_orthorhombic_symmetry.ipynb b/tutorial/7_correcting_orthorhombic_symmetry.ipynb
index a385b3d1..0525bce7 100644
--- a/tutorial/7_correcting_orthorhombic_symmetry.ipynb
+++ b/tutorial/7_correcting_orthorhombic_symmetry.ipynb
@@ -60,7 +60,7 @@
    "outputs": [],
    "source": [
     "# create sed processor using the config file with time-stamps:\n",
-    "sp = sed.SedProcessor(folder=scandir, user_config=\"../sed/config/mpes_example_config.yaml\", time_stamps=True, verbose=True)\n",
+    "sp = sed.SedProcessor(folder=scandir, user_config=\"../src/sed/config/mpes_example_config.yaml\", system_config={}, time_stamps=True, verbose=True)\n",
     "sp.add_jitter()"
    ]
   },
@@ -216,11 +216,8 @@
   }
  ],
  "metadata": {
-  "interpreter": {
-   "hash": "728003ee06929e5fa5ff815d1b96bf487266025e4b7440930c6bf4536d02d243"
-  },
   "kernelspec": {
-   "display_name": "python3",
+   "display_name": ".pyenv",
    "language": "python",
    "name": "python3"
   },
diff --git a/tutorial/8_jittering_tutorial.ipynb b/tutorial/8_jittering_tutorial.ipynb
index ef11af7a..2dd8f761 100644
--- a/tutorial/8_jittering_tutorial.ipynb
+++ b/tutorial/8_jittering_tutorial.ipynb
@@ -58,7 +58,7 @@
    "outputs": [],
    "source": [
     "# create sed processor using the config file:\n",
-    "sp = sed.SedProcessor(folder=scandir, config=\"../sed/config/mpes_example_config.yaml\")"
+    "sp = sed.SedProcessor(folder=scandir, config=\"../src/sed/config/mpes_example_config.yaml\", system_config={})"
    ]
   },
   {
@@ -358,11 +358,8 @@
   }
  ],
  "metadata": {
-  "interpreter": {
-   "hash": "728003ee06929e5fa5ff815d1b96bf487266025e4b7440930c6bf4536d02d243"
-  },
   "kernelspec": {
-   "display_name": "python3",
+   "display_name": ".pyenv",
    "language": "python",
    "name": "python3"
   },
diff --git a/tutorial/9_hextof_workflow_trXPD.ipynb b/tutorial/9_hextof_workflow_trXPD.ipynb
index 90f3ef63..c5e93408 100644
--- a/tutorial/9_hextof_workflow_trXPD.ipynb
+++ b/tutorial/9_hextof_workflow_trXPD.ipynb
@@ -95,7 +95,7 @@
    "outputs": [],
    "source": [
     "# pick the default configuration file for hextof@FLASH\n",
-    "config_file = Path('../sed/config/flash_example_config.yaml')\n",
+    "config_file = Path('../src/sed/config/flash_example_config.yaml')\n",
     "assert config_file.exists()"
    ]
   },
@@ -111,8 +111,8 @@
     "    \"core\": {\n",
     "        \"beamtime_id\": 11019101,\n",
     "        \"paths\": {\n",
-    "            \"data_raw_dir\": path,\n",
-    "            \"data_parquet_dir\": buffer_path\n",
+    "            \"raw\": path,\n",
+    "            \"processed\": buffer_path\n",
     "        },\n",
     "    },\n",
     "}"
@@ -142,25 +142,31 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "en_cal_config = {\n",
-    "    'energy': {\n",
-    "        'calibration': {\n",
-    "            'E0': -54.971004271795664,\n",
-    "            'creation_date': 1718801358.232129,\n",
-    "            'd': 0.8096677238144319,\n",
-    "            'energy_scale': 'kinetic',\n",
-    "            't0': 4.0148196706891397e-07,\n",
-    "            'calib_type': 'fit',\n",
-    "            'fit_function': '(a0/(x0-a1))**2 + a2',\n",
-    "            'coefficients': ([ 8.09667724e-01,  4.01481967e-07, -5.49710043e+01]),\n",
-    "            'axis': 0},\n",
-    "        'tof': None,\n",
-    "        'offsets': {\n",
-    "            'constant': -76.5,\n",
-    "            'creation_date': 1718801360.817963,\n",
-    "            'monochromatorPhotonEnergy': {'preserve_mean': True,'reduction': None,'weight': -1},\n",
-    "            'sampleBias': {'preserve_mean': False, 'reduction': None, 'weight': 1},\n",
-    "            'tofVoltage': {'preserve_mean': True, 'reduction': None, 'weight': -1}}}}"
+    "energy_cal = {\n",
+    "    \"energy\": {\n",
+    "        \"calibration\": {\n",
+    "            \"E0\": -132.47100427179566,\n",
+    "            \"creation_date\": '2024-11-30T20:47:03.305244',\n",
+    "            \"d\": 0.8096677238144319,\n",
+    "            \"energy_scale\": \"kinetic\",\n",
+    "            \"t0\": 4.0148196706891397e-07,\n",
+    "        },\n",
+    "        \"offsets\":{\n",
+    "            \"constant\": 1,\n",
+    "            \"creation_date\": '2024-11-30T21:17:07.762199',\n",
+    "            \"columns\": {\n",
+    "                \"monochromatorPhotonEnergy\": {\n",
+    "                    \"preserve_mean\": True,\n",
+    "                    \"weight\": -1,\n",
+    "                },\n",
+    "                \"tofVoltage\": {\n",
+    "                    \"preserve_mean\": True,\n",
+    "                    \"weight\": -1,\n",
+    "                },\n",
+    "            },\n",
+    "        },\n",
+    "    },\n",
+    "}"
    ]
   },
   {
@@ -180,7 +186,7 @@
    "outputs": [],
    "source": [
     "run_number = 44498\n",
-    "sp_44498 = SedProcessor(runs=[run_number], folder_config=en_cal_config, config=config_override, system_config=config_file, verbose=True)\n",
+    "sp_44498 = SedProcessor(runs=[run_number], folder_config=energy_cal, config=config_override, system_config=config_file, verbose=True)\n",
     "sp_44498.add_jitter()"
    ]
   },